Bacteria go to extremes to handle hard times: They hunker down, building a fortress-like shell around their DNA and turning off all signs of life. And yet, when times improve, these dormant spores can rise from the seeming dead.
But “you gotta be careful when you decide to come back to life,” says Peter Setlow, a biochemist at UConn Health in Farmington. “Because if you get it wrong, you die.” How is a spore to tell?
For spores of the bacterium Bacillus subtilis, the solution is simple: It counts.
These “living rocks” sense it’s time to revive, or germinate, by essentially counting how often they encounter nutrients, researchers report in a new study in the Oct. 7 Science. “They appear to have literally no measurable biological activity,” says Gürol Süel, a microbiologist at the University of California, San Diego. But Süel and his colleagues knew that spores’ cores contain positively charged potassium atoms, and because these atoms can move around without the cell using energy, the team suspected that potassium could be involved in shocking the cells awake.
So the team exposed B. subtilis spores to nutrients and used colorful dyes to track the movement of potassium out of the core. With each exposure, more potassium left the core, shifting its electrical charge to be more negative. Once the spores’ cores were negatively charged enough, germination was triggered, like a champagne bottle finally popping its cork. The number of exposures it took to trigger germination varied by spore, just like some corks require more or less twisting to pop. Spores whose potassium movement was hamstrung showed limited change in electric charge and were less likely to “pop” back to life no matter how many nutrients they were exposed to, the team’s experiments showed.
Changes in the electrical charge of a cell are important across the tree of life, from determining when brain cells zip off messages to each other, to the snapping of a Venus flytrap (SN: 10/14/20). Finding that spores also use electrical charges to set their wake-up calls excites Süel. “You want to find principles in biology,” he says, “processes that cross systems, that cross fields and boundaries.”
Spores are not only interesting for their unique and extreme biology, but also for practical applications. Some “can cause some rather nasty things” from food poisoning to anthrax, says Setlow, who was not involved in the study. Since spores are resistant to most antibiotics, understanding germination could lead to a way to bring them back to life in order to kill them for good.
Still, there are many unanswered questions about the “black box” of how spores start germination, like whether it’s possible for the spores to “reset” their potassium count. “We really are in the beginnings of trying to fill in that black box,” says Kaito Kikuchi, a biologist now at Reveal Biosciences in San Diego who conducted the work while at University of California, San Diego. But discovering how spores manage to track their environment while more dead than alive is an exciting start.
Humankind is seeing Neptune’s rings in a whole new light thanks to the James Webb Space Telescope.
In an infrared image released September 21, Neptune and its gossamer diadems of dust take on an ethereal glow against the inky backdrop of space. The stunning portrait is a huge improvement over the rings’ previous close-up, which was taken more than 30 years ago.
Unlike the dazzling belts encircling Saturn, Neptune’s rings appear dark and faint in visible light, making them difficult to see from Earth. The last time anyone saw Neptune’s rings was in 1989, when NASA’s Voyager 2 spacecraft, after tearing past the planet, snapped a couple grainy photos from roughly 1 million kilometers away (SN: 8/7/17). In those photos, taken in visible light, the rings appear as thin, concentric arcs.
As Voyager 2 continued to interplanetary space, Neptune’s rings once again went into hiding — until July. That’s when the James Webb Space Telescope, or JWST, turned its sharp, infrared gaze toward the planet from roughly 4.4 billion kilometers away (SN: 7/11/22). Neptune itself appears mostly dark in the new image. That’s because methane gas in the planet’s atmosphere absorbs much of its infrared light. A few bright patches mark where high-altitude methane ice clouds reflect sunlight.
And then there are the ever-elusive rings. “The rings have lots of ice and dust in them, which are extremely reflective in infrared light,” says Stefanie Milam, a planetary scientist at NASA’s Goddard Space Flight Center in Greenbelt, Md., and one of JWST’s project scientists. The enormity of the telescope’s mirror also makes its images extra sharp. “JWST was designed to look at the first stars and galaxies across the universe, so we can really see fine details that we haven’t been able to see before,” Milam says.
Upcoming JWST observations will look at Neptune with other scientific instruments. That should provide new intel on the rings’ composition and dynamics, as well as on how Neptune’s clouds and storms evolve, Milam says. “There’s more to come.”
As people around the world marveled in July at the most detailed pictures of the cosmos snapped by the James Webb Space Telescope, biologists got their first glimpses of a different set of images — ones that could help revolutionize life sciences research.
The images are the predicted 3-D shapes of more than 200 million proteins, rendered by an artificial intelligence system called AlphaFold. “You can think of it as covering the entire protein universe,” said Demis Hassabis at a July 26 news briefing. Hassabis is cofounder and CEO of DeepMind, the London-based company that created the system. Combining several deep-learning techniques, the computer program is trained to predict protein shapes by recognizing patterns in structures that have already been solved through decades of experimental work using electron microscopes and other methods. The AI’s first splash came in 2021, with predictions for 350,000 protein structures — including almost all known human proteins. DeepMind partnered with the European Bioinformatics Institute of the European Molecular Biology Laboratory to make the structures available in a public database.
July’s massive new release expanded the library to “almost every organism on the planet that has had its genome sequenced,” Hassabis said. “You can look up a 3-D structure of a protein almost as easily as doing a key word Google search.”
These are predictions, not actual structures. Yet researchers have used some of the 2021 predictions to develop potential new malaria vaccines, improve understanding of Parkinson’s disease, work out how to protect honeybee health, gain insight into human evolution and more. DeepMind has also focused AlphaFold on neglected tropical diseases, including Chagas disease and leishmaniasis, which can be debilitating or lethal if left untreated. The release of the vast dataset was greeted with excitement by many scientists. But others worry that researchers will take the predicted structures as the true shapes of proteins. There are still things AlphaFold can’t do — and wasn’t designed to do — that need to be tackled before the protein cosmos completely comes into focus.
Having the new catalog open to everyone is “a huge benefit,” says Julie Forman-Kay, a protein biophysicist at the Hospital for Sick Children and the University of Toronto. In many cases, AlphaFold and RoseTTAFold, another AI researchers are excited about, predict shapes that match up well with protein profiles from experiments. But, she cautions, “it’s not that way across the board.”
Predictions are more accurate for some proteins than for others. Erroneous predictions could leave some scientists thinking they understand how a protein works when really, they don’t. Painstaking experiments remain crucial to understanding how proteins fold, Forman-Kay says. “There’s this sense now that people don’t have to do experimental structure determination, which is not true.” Plodding progress Proteins start out as long chains of amino acids and fold into a host of curlicues and other 3-D shapes. Some resemble the tight corkscrew ringlets of a 1980s perm or the pleats of an accordion. Others could be mistaken for a child’s spiraling scribbles.
A protein’s architecture is more than just aesthetics; it can determine how that protein functions. For instance, proteins called enzymes need a pocket where they can capture small molecules and carry out chemical reactions. And proteins that work in a protein complex, two or more proteins interacting like parts of a machine, need the right shapes to snap into formation with their partners.
Knowing the folds, coils and loops of a protein’s shape may help scientists decipher how, for example, a mutation alters that shape to cause disease. That knowledge could also help researchers make better vaccines and drugs.
For years, scientists have bombarded protein crystals with X-rays, flash frozen cells and examined them under highpowered electron microscopes, and used other methods to discover the secrets of protein shapes. Such experimental methods take “a lot of personnel time, a lot of effort and a lot of money. So it’s been slow,” says Tamir Gonen, a membrane biophysicist and Howard Hughes Medical Institute investigator at the David Geffen School of Medicine at UCLA. Such meticulous and expensive experimental work has uncovered the 3-D structures of more than 194,000 proteins, their data files stored in the Protein Data Bank, supported by a consortium of research organizations. But the accelerating pace at which geneticists are deciphering the DNA instructions for making proteins has far outstripped structural biologists’ ability to keep up, says systems biologist Nazim Bouatta of Harvard Medical School. “The question for structural biologists was, how do we close the gap?” he says.
For many researchers, the dream has been to have computer programs that could examine the DNA of a gene and predict how the protein it encodes would fold into a 3-D shape.
Here comes AlphaFold Over many decades, scientists made progress toward that AI goal. But “until two years ago, we were really a long way from anything like a good solution,” says John Moult, a computational biologist at the University of Maryland’s Rockville campus.
Moult is one of the organizers of a competition: the Critical Assessment of protein Structure Prediction, or CASP. Organizers give competitors a set of proteins for their algorithms to fold and compare the machines’ predictions against experimentally determined structures. Most AIs failed to get close to the actual shapes of the proteins. Then in 2020, AlphaFold showed up in a big way, predicting the structures of 90 percent of test proteins with high accuracy, including two-thirds with accuracy rivaling experimental methods.
Deciphering the structure of single proteins had been the core of the CASP competition since its inception in 1994. With AlphaFold’s performance, “suddenly, that was essentially done,” Moult says.
Since AlphaFold’s 2021 release, more than half a million scientists have accessed its database, Hassabis said in the news briefing. Some researchers, for example, have used AlphaFold’s predictions to help them get closer to completing a massive biological puzzle: the nuclear pore complex. Nuclear pores are key portals that allow molecules in and out of cell nuclei. Without the pores, cells wouldn’t work properly. Each pore is huge, relatively speaking, composed of about 1,000 pieces of 30 or so different proteins. Researchers had previously managed to place about 30 percent of the pieces in the puzzle. That puzzle is now almost 60 percent complete, after combining AlphaFold predictions with experimental techniques to understand how the pieces fit together, researchers reported in the June 10 Science.
Now that AlphaFold has pretty much solved how to fold single proteins, this year CASP organizers are asking teams to work on the next challenges: Predict the structures of RNA molecules and model how proteins interact with each other and with other molecules.
For those sorts of tasks, Moult says, deep-learning AI methods “look promising but have not yet delivered the goods.”
Where AI falls short Being able to model protein interactions would be a big advantage because most proteins don’t operate in isolation. They work with other proteins or other molecules in cells. But AlphaFold’s accuracy at predicting how the shapes of two proteins might change when the proteins interact are “nowhere near” that of its spot-on projections for a slew of single proteins, says Forman-Kay, the University of Toronto protein biophysicist. That’s something AlphaFold’s creators acknowledge too.
The AI trained to fold proteins by examining the contours of known structures. And many fewer multiprotein complexes than single proteins have been solved experimentally. Forman-Kay studies proteins that refuse to be confined to any particular shape. These intrinsically disordered proteins are typically as floppy as wet noodles (SN: 2/9/13, p. 26). Some will fold into defined forms when they interact with other proteins or molecules. And they can fold into new shapes when paired with different proteins or molecules to do various jobs.
AlphaFold’s predicted shapes reach a high confidence level for about 60 percent of wiggly proteins that Forman-Kay and colleagues examined, the team reported in a preliminary study posted in February at bioRxiv.org. Often the program depicts the shapeshifters as long corkscrews called alpha helices.
Forman-Kay’s group compared AlphaFold’s predictions for three disordered proteins with experimental data. The structure that the AI assigned to a protein called alpha-synuclein resembles the shape that the protein takes when it interacts with lipids, the team found. But that’s not the way the protein looks all the time.
For another protein, called eukaryotic translation initiation factor 4E-binding protein 2, AlphaFold predicted a mishmash of the protein’s two shapes when working with two different partners. That Frankenstein structure, which doesn’t exist in actual organisms, could mislead researchers about how the protein works, Forman-Kay and colleagues say. AlphaFold may also be a little too rigid in its predictions. A static “structure doesn’t tell you everything about how a protein works,” says Jane Dyson, a structural biologist at the Scripps Research Institute in La Jolla, Calif. Even single proteins with generally well-defined structures aren’t frozen in space. Enzymes, for example, undergo small shape changes when shepherding chemical reactions.
If you ask AlphaFold to predict the structure of an enzyme, it will show a fixed image that may closely resemble what scientists have determined by X-ray crystallography, Dyson says. “But [it will] not show you any of the subtleties that are changing as the different partners” interact with the enzyme.
“The dynamics are what Mr. AlphaFold can’t give you,” Dyson says.
A revolution in the making The computer renderings do give biologists a head start on solving problems such as how a drug might interact with a protein. But scientists should remember one thing: “These are models,” not experimentally deciphered structures, says Gonen, at UCLA.
He uses AlphaFold’s protein predictions to help make sense of experimental data, but he worries that researchers will accept the AI’s predictions as gospel. If that happens, “the risk is that it will become harder and harder and harder to justify why you need to solve an experimental structure.” That could lead to reduced funding, talent and other resources for the types of experiments needed to check the computer’s work and forge new ground, he says. Harvard Medical School’s Bouatta is more optimistic. He thinks that researchers probably don’t need to invest experimental resources in the types of proteins that AlphaFold does a good job of predicting, which should help structural biologists triage where to put their time and money.
“There are proteins for which AlphaFold is still struggling,” Bouatta agrees. Researchers should spend their capital there, he says. “Maybe if we generate more [experimental] data for those challenging proteins, we could use them for retraining another AI system” that could make even better predictions.
He and colleagues have already reverse engineered AlphaFold to make a version called OpenFold that researchers can train to solve other problems, such as those gnarly but important protein complexes.
Massive amounts of DNA generated by the Human Genome Project have made a wide range of biological discoveries possible and opened up new fields of research (SN: 2/12/22, p. 22). Having structural information on 200 million proteins could be similarly revolutionary, Bouatta says.
In the future, thanks to AlphaFold and its AI kin, he says, “we don’t even know what sorts of questions we might be asking.”
The answer to one of the greatest mysteries of the universe may come down to one of the smallest, and spookiest, particles.
Matter is common in the cosmos. Everything around us — from planets to stars to puppies — is made up of matter. But matter has a flip side: antimatter. Protons, electrons and other particles all have antimatter counterparts: antiprotons, positrons, etc. Yet for some reason antimatter is much rarer than matter — and no one knows why. Physicists believe the universe was born with equal amounts of matter and antimatter. Since matter and antimatter counterparts annihilate on contact, that suggests the universe should have ended up with nothing but energy. Something must have tipped the balance.
Some physicists think lightweight subatomic particles called neutrinos could point to an answer. These particles are exceedingly tiny, with less than a millionth the mass of an electron (SN: 4/21/21). They’re produced in radioactive decays and in the sun and other cosmic environments. Known for their ethereal tendency to evade detection, neutrinos have earned the nickname “ghost particles.” These spooky particles, originally thought to have no mass at all, have a healthy track record of producing scientific surprises (SN: 10/6/15).
Now researchers are building enormous detectors to find out if neutrinos could help solve the mystery of the universe’s matter. The Hyper-Kamiokande experiment in Hida City, Japan, and the Deep Underground Neutrino Experiment in Lead, S.D., will study neutrinos and their antimatter counterparts, antineutrinos. A difference in neutrinos’ and antineutrinos’ behavior might hint at the origins of the matter-antimatter imbalance, scientists suspect.
Watch the video below to find out how neutrinos might reveal why the universe contains, well, anything at all.
Face masks — the unofficial symbol of the COVID-19 pandemic — are leveling up.
A mask outfitted with special electronics can detect SARS-CoV-2, the virus that causes COVID-19, and other airborne viruses within 10 minutes of exposure, materials researcher Yin Fang and colleagues report September 19 in Matter.
“The lightness and wearability of this face mask allows users to wear it anytime, anywhere,” says Fang, of Tongji University in Shanghai. “It’s expected to serve as an early warning system to prevent large outbreaks of respiratory infectious diseases.” Airborne viruses can hitch a ride between hosts in the air droplets that people breathe in and out. People infected with a respiratory illness can expel thousands of virus-containing droplets by talking, coughing and sneezing. Even those with no signs of being sick can sometimes pass on these viruses; people who are infected with SARS-CoV-2 can start infecting others at least two to three days before showing symptoms (SN: 3/13/20). So viruses often have a head start when it comes to infecting new people.
Fang and his colleagues designed a special sensor that reacts to the presence of certain viral proteins in the air and attached it to a face mask. The team then spritzed droplets containing proteins produced by the viruses that cause COVID-19, bird flu or swine flu into a chamber with the mask.
The sensor could detect just a fraction of a microliter of these proteins — a cough might contain 10 to 80 times as much. Once a pathogen was detected, the sensor-mask combo sent a signal to the researchers informing them of the virus’s presence. Ultimately, the researchers plan for such signals to be sent to a wearer’s phone or other devices. By combining this technology with more conventional testing, the team says, health care providers and public health officials might be able to better contain future pandemics.
Getting out into society after a long isolation gets awkward. Ask the Pahrump poolfish, loners in a desert for some 10,000 years.
This hold-in-your-hand-size fish (Empetrichthys latos) has a chubby, torpedo shape and a mouth that looks as if it’s almost smiling. Until the 1950s, this species had three forms, each evolving in its own spring. Now only one survives, which developed in a spring-fed oasis in the Mojave Desert’s Pahrump Valley, about an hour’s drive west of Las Vegas.
Fish in a desert are not that weird when you take the long view (SN: 1/26/16). In a former life, some desert valleys were ancient lakes. As the region’s lakes dried up, fish got stuck in the remaining puddles. Various stranded species over time adapted to quirks of their private microlakes, and a desert-fish version of the Galapagos Islands’ diverse finches arose. “We like to say that Darwin, if he had a different travel agent, could have come to the same conclusions just from the desert,” says evolutionary biologist Craig Stockwell of North Dakota State University in Fargo.
The desert “island” where E. latos evolved was Manse Spring on a private ranch. From a distance, the spring looked “just like a little clump of trees,” remembers ecologist Shawn Goodchild, who is now based in Lake Park, Minn. The spot of desert greenery surrounded the Pahrump poolfish’s entire native range, about the length of an Olympic swimming pool.
By the 1960s, biologists feared the fish were doomed. The spring’s flow rate had dropped some 70 percent as irrigation for farms in the desert sucked away water. And disastrous predators arrived: a kid’s discarded goldfish. Conservation managers fought back, but neither poison nor dynamite wiped out the newcomers. And then in August of 1975, Manse Spring dried up.
Conservation managers had moved some poolfish to other springs, but the long-isolated species just didn’t seem to get the dangers of living with other kinds of fishes. The poolfish were easily picked off by predators in their new home.
Lab tests of fake fish-murder scenes may help explain why. For instance, researchers tainted aquarium water with pureed fish bits. In an expected reaction, fathead minnows (Pimephales promelas) freaked at traces of dead minnow drifting through the water and huddled low in the tank. The Pahrump poolfish in water tainted with blender-whizzed skin of their kind just kept swimming around the upper waters as if corpse taint were no scarier than tap water. Literally. Stockwell and colleagues can say that because they ran a fear test with nonscary dechlorinated tap water. Poolfish didn’t huddle then either, the team reports in the Aug. 31 Proceedings of the Royal Society B.
Then, however, Stockwell and a colleague were musing about some rescued poolfish in cattle tanks when nearby dragonflies caught the researchers’ attention.
Before dragonflies mature into shimmering aerial marvels, the young prowl underwater as violent predators. In moves worthy of scary aliens in a sci-fi movie, many dragonfly nymphs can shoot their jaws out from their head to scoop up prey, including fish eggs and fish larvae. With young dragonflies prowling a pool’s bottom and plants, poolfish moving up the water column “would be a good way to reduce their risk,” Stockwell says. Testing of that idea has begun.
Fish that people thought were foolishly naïve may just be savvy in a different way. Especially after isolation in a desert with dragons.
Chicxulub, the asteroid that wiped out most dinosaurs, might have had a little sibling.
Off the coast of West Africa, hundreds of meters beneath the seafloor, scientists have identified what appears to be the remains of an 8.5-kilometer-wide impact crater, which they’ve named Nadir. The team estimates that the crater formed roughly around the same time that another asteroid — Chicxulub, the dinosaur killer — slammed into modern day Mexico (SN: 1/25/17). If confirmed, it could mean that nonbird dinosaurs met their demise by a one-two punch of asteroids, researchers report in the Aug. 17 Science Advances. “The idea that [Chicxulub] had help — for want of a better phrase — would have really added insult to serious injury,” says study coauthor Veronica Bray, a planetary scientist at the University of Arizona in Tucson.
Nearly 200 impact craters have been discovered on Earth (SN: 12/18/18), the vast majority of which are on land. That’s because impact craters at sea gradually become buried under sediment, Bray says, which makes the Nadir structure a valuable scientific find, regardless of its birthdate.
Geologist Uisdean Nicholson of Heriot-Watt University in Edinburgh happened upon the structure while analyzing data collected by seismic waves transmitted underground to detect physical structures offshore of Guinea. Lurking beneath the seafloor — and under nearly 1 kilometer of water — he discerned a bowl-shaped structure with a broken-up, terraced floor and a pronounced central peak — features expected of a large impact.
Based on the structure’s dimensions, Bray, Nicholson and their colleagues calculate that, if an asteroid was responsible for the terrain, it would probably have been over 400 meters wide. What’s more, the researchers estimate that the impact would have rocked the ground like a magnitude 7 earthquake and stirred tsunamis hundreds of meters high.
Despite that fallout, the Nadir impact would have been far less devastating than the one from the roughly 10-kilometer-wide Chicxulub asteroid, says Michael Rampino, a geologist from New York University who was not involved in the study. “It certainly wouldn’t have had global effects,” he says.
Using geologic layers adjacent to Nadir, some with ages obtained by past studies, the team estimated the structure to have formed around the end of the Cretaceous period — 66 million years ago. The Nadir asteroid may even have formed a pair with the Chicxulub asteroid, the two having been ripped apart by gravitational forces during a previous Earth flyby, the researchers speculate. But the study’s conclusions have some experts wary. “It looks like an impact crater, but it could also be something else,” says geologist Philippe Claeys of Vrije Universiteit Brussel in Belgium, who was not involved in the research. Confirming that the structure is an impact crater will require drilling for solid evidence, such as shocked quartz, he says. Alternative explanations for the structure’s identity include a collapsed volcanic caldera or a squeezed body of salt called a salt diapir.
The Nadir structure’s age is another uncertainty. The seismic data shows it appears to have formed sometime near the end the Cretaceous period or maybe a little later, Claeys says. “But that’s around the best they can say.” Drilling in the crater for minerals that contain radioactive elements could provide a more precise date of formation, Rampino says.
It’s not the first time that scientists have investigated whether Chicxulub had an accomplice. Some studies have suggested that the Boltysh crater in Ukraine may have formed at the same time as Chicxulub, though researchers have since determined that Boltysh formed 650,000 years later.
Bray and her colleagues are currently negotiating for funding to collect samples from the crater, with aspirations to drill in 2024. That will hopefully settle some of the debate surrounding Nadir’s origins, Bray says, though new questions will probably arise too. “If we do prove that this is the sister of the dinosaur killer, then how many other siblings are there?”
Sea urchin skeletons may owe some of their strength to a common geometric design.
Components of the skeletons of common sea urchins (Paracentrotus lividus) follow a similar pattern to that found in honeycombs and dragonfly wings, researchers report in the August Journal of the Royal Society Interface. Studying this recurring natural order could inspire the creation of strong yet lightweight new materials.
Urchin skeletons display “an incredible diversity of structures at the microscale, varying from fully ordered to entirely chaotic,” says marine biologist and biomimetic consultant Valentina Perricone. These structures may help the animals maintain their shape when faced with predator attacks and environmental stresses.
While using a scanning electron microscope to study urchin skeleton tubercules — sites where the spines attach that withstand strong mechanical forces — Perricone spotted “a curious regularity.” Tubercules seem to follow a type of common natural order called a Voronoi pattern, she and her colleagues found. Using math, a Voronoi pattern is created by a process that divides a region into polygon-shaped cells that are built around points within them called seeds (SN: 9/23/18). The cells follow the nearest neighbor rule: Every spot inside a cell is nearer to that cell’s seed than to any other seed. Also, the boundary that separates two cells is equidistant from both their seeds.
A computer-generated Voronoi pattern had an 82 percent match with the pattern found in sea urchin skeletons. This arrangement, the team suspects, yields a strong yet lightweight skeletal structure. The pattern “can be interpreted as an evolutionary solution” that “optimizes the skeleton,” says Perricone, of the University of Campania “Luigi Vanvitelli” in Aversa, Italy.
Urchins, dragonflies and bees aren’t the only beneficiaries of Voronoi architecture. “We are developing a library of bioinspired, Voronoi-based structures” that could “serve as lightweight and resistant solutions” for materials design, Perricone says. These, she hopes, could inspire new developments in materials science, aerospace, architecture and construction.
It’s not easy being ringed. A newly released image from the James Webb Space Telescope, or JWST, shows the Cartwheel Galaxy still reeling from a run-in with a smaller galaxy 400 million years ago.
The Cartwheel Galaxy, so called because of its bright inner ring and colorful outer ring, lies about 500 million light-years from Earth. Astronomers think it used to be a large spiral like the Milky Way, until a smaller galaxy smashed through it. In earlier observations with other telescopes, the space between the rings appeared shrouded in dust.
Now, JWST’s infrared cameras have peered through the dust and found previously unseen stars and structure (SN: 7/11/22). The new image shows sites of intense star formation throughout the galaxy that were triggered by the collision’s aftereffects. Some of those new stars are forming in spokelike patterns between the central ring and the outer ring, a process that is not well understood. Ring galaxies are rare, and galaxies with two rings are even more unusual. That strange shape means that the long-ago collision set up multiple waves of gas rippling back and forth in the galaxy left behind. It’s like if you drop a pebble in the bathtub, says JWST project scientist Klaus Pontoppidan of the Space Telescope Science Institute in Baltimore. “First you get this ring, then it hits the walls of your bathtub and reflects back, and you get a more complicated structure.”
The effect probably means that the Cartwheel Galaxy has a long road to recovery ahead — and astronomers don’t know what it will look like in the end.
As for the smaller galaxy that caused all this mayhem, it didn’t stick around to get its picture taken. “It’s gone off on its merry way,” Pontoppidan says.
Lionfish certainly aren’t the fastest predators on the reef, but new research suggests that they can catch swift prey through pure tenacity, gliding slowly in pursuit until the perfect moment to strike.
The finding may help explain part of the lionfish’s impact as an invasive species, and reveal a key hunting strategy that other relatively slow predators use, researchers report August 2 in Proceedings of the Royal Society B.
Festooned with long striped spines, lionfish can make their surreal silhouettes disappear against a coral reef backdrop long enough to stalk and ambush small fish. But the predators also feed in open water where they’re more visible. Curious about how the predators hunt in plain view, Ashley Peterson, a comparative biomechanist at the University of California, Irvine, and her colleagues placed red lionfish (Pterois volitans) in a tank and recorded them as they chased down a green chromis (Chromis viridis), a small reef fish.
In 14 of the 23 trials, the lionfish successfully gulped down their prey. They also had a high rate of strike success, capturing the chromis in 74 percent of the trials where the lionfish made a strike attempt.
On average, the chromis swam about twice as fast as the lionfish. But many still fell victim to what Peterson and biomechanist Matthew McHenry, also at the University of California, Irvine, call a persistent-predation strategy — the lionfish swim toward a chromis, aiming for its current position, not the direction to intercept its path. And the lionfish’s pursuit is steady and incessant, the team found.
“If they’re interested in something and they want to try to eat it, they just seem to not give up,” Peterson says.
In contrast, the prey fish does bursts of fast swimming along with short pauses.
“Over time, all those pauses add up and allow this lionfish to get closer and closer and closer,” Peterson says. Then the slightest mistake or bit of distraction can doom the prey to the lionfish’s suction-creating jaws.
“This is a good example of ‘slow and steady wins the race,’” says Bridie Allan, a marine ecologist at the University of Otago in Dunedin, New Zealand who was not involved in the research. It would be interesting to see how the unwavering chase plays out in the wild, where there are no spatial restrictions like in a tank, she says.
If lionfish do use the strategy in the wild and prey react similarly, it’s possible that the tactic could contribute to the destructive potential of their invasion in the Caribbean, Western Atlantic and the Mediterranean, where the fish are devouring native ocean animals and disrupting food webs (SN: 7/6/16). But other factors, such as the lionfish’s huge appetite or prolific reproduction, could be more influential on invasiveness.
The persistent-predation strategy may not be exclusive to lionfish, Peterson says. Other predatory fish groups with sluggish swimmers — like straw-shaped trumpetfish (Aulostomus spp.) — could also use it.
In a natural setting, prey that are dodging lionfish and other slow swimmers may have more places to hide, Peterson says. But there are inherent risks in a busy, distracting environment too. “If you’re near a reef or up against the coral, you could get pinned if you aren’t really paying attention,” she says. That’s when determined and hungry slowpokes may have the upper hand.
