Megatooth sharks get their name from their massive teeth, which can each be bigger than a human hand. The group includes Megalodon, the largest shark that ever lived, as well as several related species.

While sharks of one kind or another have existed since long before the dinosaurs — for more than 400 million years — these megatooth sharks evolved after the dinosaurs went extinct and ruled the seas until just 3 million years ago.

“We’re used to thinking of the largest species — blue whales, whale sharks, even elephants and diplodocuses — as filter feeders or herbivores, not predators,” said Emma Kast, a 2019 Ph.D. graduate in geosciences who is the first author on a new study in the current issue of Science Advances. “But Megalodon and the other megatooth sharks were genuinely enormous carnivores that ate other predators, and Meg went extinct only a few million years ago.”

Her adviser Danny Sigman, Princeton’s Dusenbury Professor of Geological and Geophysical Sciences, added, “If Megalodon existed in the modern ocean, it would thoroughly change humans’ interaction with the marine environment.”

A team of Princeton researchers has now discovered clear evidence that Megalodon and some of its ancestors were at the very highest rung of the prehistoric food chain — what scientists call the highest “trophic level.” Indeed, their trophic signature is so high that they must have eaten other predators and predators-of-predators in a complicated food web, say the researchers.

“Ocean food webs do tend to be longer than the grass-deer-wolf food chain of land animals, because you start with such small organisms,” said Kast, now at the University of Cambridge, who wrote the first iteration of this research as a chapter in her dissertation. “To reach the trophic levels we’re measuring in these megatooth sharks, we don’t just need to add one trophic level — one apex predator on top of the marine food chain — we need to add several onto the top the modern marine food web.”

Megalodon has been conservatively estimated at 15 meters long — 50 feet — while modern great white sharks typically top out around five meters (15 feet).

To reach their conclusions about the prehistoric marine food web, Kast, Sigman and their colleagues used a novel technique to measure the nitrogen isotopes in the sharks’ teeth. Ecologists have long known that the more nitrogen-15 an organism has, the higher its trophic level, but scientists have never before been able to measure the tiny amounts of nitrogen preserved in the enamel layer of these extinct predators’ teeth.

“We have a series of shark teeth from different time periods, and we were able to trace their trophic level versus their size,” said Zixuan (Crystal) Rao, a graduate student in Sigman’s research group and a co-author on the current paper.

One way to tuck in an extra trophic level or two is cannibalism, and several lines of evidence point to that in both megatooth sharks and other prehistoric marine predators.

The nitrogen time machine

Without a time machine, there’s no easy way to recreate the food webs of extinct creatures; very few bones have survived with teeth marks that say, “I was chewed on by a massive shark.”

Fortunately, Sigman and his team have spent decades developing other methods, based on the knowledge that the nitrogen isotope levels in a creature’s cells reveal whether it is at the top, middle or bottom of a food chain.

“The whole direction of my research team is to look for chemically fresh, but physically protected, organic matter — including nitrogen — in organisms from the distant geologic past,” said Sigman.

A few plants, algae and other species at the bottom of the food web have mastered the knack of turning nitrogen from the air or water into nitrogen in their tissues. Organisms that eat them then incorporate that nitrogen into their own bodies, and critically, they preferentially excrete (sometimes via urine) more of nitrogen’s lighter isotope, N-14, than its heavier cousin, N-15.

In other words, N-15 builds up, relative to N-14, as you climb up the food chain.

Other researchers have used this approach on creatures from the recent past — the most recent 10-15 thousand years — but there hasn’t been enough nitrogen left in older animals to measure, until now.

Why? Soft tissue like muscles and skin are hardly ever preserved. To complicate matters, sharks don’t have bones — their skeletons are made of cartilage.

But sharks do have one golden ticket into the fossil record: teeth. Teeth are more easily preserved than bones because they are encased in enamel, a rock-hard material that is virtually immune to most decomposing bacteria.

“Teeth are designed to be chemically and physically resistant so they can survive in the very chemically reactive environment of the mouth and break apart food that can have hard parts,” Sigman explained. And in addition, sharks aren’t limited to the 30 or so pearly whites that humans have. They are constantly growing and losing teeth — modern sand sharks lose a tooth every day of their decades-long lives, on average — which means that every shark produces thousands of teeth over its lifetime.

“When you look in the geologic record, one of the most abundant fossil types are shark teeth,” said Sigman. “And within the teeth, there is a tiny amount of organic matter that was used to build the enamel of the teeth — and is now trapped within that enamel.”

Since shark teeth are so abundant and are preserved so well, the nitrogen signatures in enamel provide a way to measure status in the food web, whether the tooth fell from a shark’s mouth millions of years ago or yesterday.

Even the largest tooth has only a thin casing of enamel, of which the nitrogen component is only a tiny trace. But Sigman’s team has been developing more and more refined techniques for extracting and measuring these nitrogen isotope ratios, and with a little help from dentist drills, cleaning chemicals and microbes that ultimately convert the nitrogen from within the enamel into nitrous oxide, they’re now able to precisely measure the N15-N14 ratio in these ancient teeth.

“We’re a little bit like a brewery,” he said. “We grow microbes and feed our samples to them. They produce nitrous oxide for us, and then we analyze the nitrous oxide they produced.”

The analysis requires a custom-built, automated nitrous oxide preparation system that extracts, purifies, concentrates and delivers the gas to a specialized stable isotope ratio mass spectrometer.

“This has been a multiple-decades-long quest that I’ve been on, to develop a core method to measure these trace amounts of nitrogen,” Sigman said. From microfossils in sediments, they moved on to other types of fossils, like corals, fish ear bones and shark teeth. “Next, we and our collaborators are applying this to mammalian teeth and dinosaur teeth.”

A deep dive into the literature during lockdown

Early in the pandemic, while her friends were making sourdough starters and bingeing Netflix, Kast pored through the ecologic literature to look for nitrogen isotope measurements of modern marine animals.

“One of the cool things that Emma did was really dig into the literature — all the data that’s been published over decades — and relate that to the fossil record,” said Michael (Mick) Griffiths, a paleoclimatologist and geochemist at William Patterson University and a co-author on the paper.

As Kast quarantined at home, she painstakingly built up a record with more than 20,000 marine mammal individuals and more than 5,000 sharks. She wants to take things much further. “Our tool has the potential to decode ancient food webs; what we need now is samples,” said Kast. “I’d love to find a museum or other archive with a snapshot of an ecosystem — a collection of different kinds of fossils from one time and place, from forams near the very base of the food web, to otoliths — inner ear bones — from different kinds of fish, to teeth from marine mammals, plus shark teeth. We could do the same nitrogen isotope analysis and put together the whole story of an ancient ecosystem.”

In addition to the literature search, their database includes their own samples of shark teeth. Co-author Kenshu Shimada of DePaul University connected with aquariums and museums, while co-authors Martin Becker of William Patterson University and Harry Maisch of Florida Gulf Coast University gathered megatooth specimens on the sea floor.

“It’s really dangerous; Harry’s a dive master, and you really need to be an expert to get these,” said Griffiths. “You can find little shark teeth on the beach, but to get the best-preserved samples, you need to go down to the bottom of the ocean. Marty and Harry have collected teeth from all over the place.”

Without clear road signs to indicate where native islanders were traveling, however, the task of reconstructing ancient trade routes relies on subtle clues locked away in the archaeological record. Researchers at the Florida Museum of Natural History recently turned to pottery to tease apart the navigational history of the Caribbean, analyzing the composition of 96 fired clay fragments across 11 islands.

The study, published in the Journal of Archaeological Science: Reports, is the broadest of its kind yet conducted in the Greater Antilles and marks the first time that pottery artifacts from the Lucayan Islands — The Bahamas plus the Turks and Caicos Islands — have been analyzed to determine their elemental composition and origin.

“Our methods mark a big improvement over other studies that mostly look at a single site or single island, where you might see differences but not know what it means because you’re looking at the results in isolation,” said co-author Lindsay Bloch, a courtesy faculty member with the Florida Museum’s Ceramic Technology Lab.

People have lived on the Caribbean islands on and off for more than 7,000 years, migrating in waves from Central and South America. As early as 800 B.C., new groups arrived from Venezuela and established a trading network among islands, which they used to exchange food, tools and jewelry. But the most common artifacts that survived to the present are the pottery vessels these objects were carried in.

“Most materials don’t preserve well in the Caribbean because of the warm, humid environment, but pottery is durable, so it ends up being one of the most common things we find,” said lead author Emily Kracht, a collections assistant in the Ceramic Technology Lab.

Over the ensuing millennia, different Caribbean cultures developed unique styles and techniques for constructing their pottery. Some artifacts are simple and unadorned, while others are highly decorated, with a lattice of incised lines, punctations, raised ridges and flared rims.

Many studies have relied almost entirely on similarities in style to distinguish between different cultures and infer their movements. But, as Bloch explains, this method has often left more questions than answers and excludes material with potentially valuable information.

“The vast majority of pottery that we find anywhere in the world is going to be undecorated. It’s going to be things used for cooking or storage, which are typically plain and often get ignored because they’re seen as generic,” she said.

Rather than studying the minutiae of varying styles, the researchers focused instead on what the pottery was made of. Using a laser to etch microscopic lines into their samples, the researchers determined the exact amounts and identities of each element in the clay used to make the pottery. Their final analysis included more than seven decades’ worth of archaeological collections that span over 1,000 years of Indigenous Caribbean history.

“One of the advantages of elemental analysis is that we’re explicitly looking for differences, which allows us to see where a pot was made and compare that to where it ended up,” Bloch said.

Such detailed comparisons are possible due to the complexity of the Caribbean’s underlying geology. The largest islands in the archipelago likely got their start as an ancient underwater plateau in the Pacific Ocean. After the breakup of the supercontinent Pangaea, the Caribbean plate drifted east in a flurry of volcanic eruptions that elevated the plateau above sea level before ultimately reaching its current position in the Atlantic.

Millions of years of weathering reduced these volcanic outcrops into fine-grained clays with differing concentrations of elements like copper, nickel, chromium and antimony. These differences mean that even the smallest Caribbean pottery sherd bears the elemental signature of the region it was made in.

The results of researchers’ comparative analysis aren’t what might be expected by simply looking at a map. The Lucayan Islands were initially used only temporarily for harvesting resources, and the people who traveled to them would have set sail from the larger islands to the south that supported permanent population centers.

Cuba might initially seem like it’s the perfect staging ground for these operations, being by far the largest Caribbean island and the closest to The Bahamas. While people did make the trek across open water from Cuba, the results of the study indicate the Caribbean’s cultural hub was instead centered on the northwest coast of Hispaniola, from which people imported and exported goods for hundreds of years.

“At least some of the pottery would have been used to ferry goods out to these islands, and people would potentially carry back a variety of marine resources,” Bloch said.

People eventually struck up permanent settlements in The Bahamas and Turks and Caicos, becoming collectively known as the Lucayans, or the People of the Islands. They began making their own pottery from claylike soils deposited by African dust plumes blown in from the Saharan Desert, but the results didn’t quite hold up to the pottery from Hispaniola — literally. Lucayan pottery, called Palmetto Ware, is most often thick and soft and crumbles over time due to the poor quality of the grainy Saharan soil.

Thus, up until the arrival of the Spanish, Hispaniola remained the main trading partner and exporter of pottery to the Lucayan Islands.

“We knew that the Lucayans were related to people in Hispaniola, and this study shows their enduring relationship over hundreds of years through pottery,” Kracht said.

William Keegan of the Florida Museum of Natural History is also a co-author on the study.

The new tool, called LANTERN, could prove useful in work ranging from producing biofuels to improving crops to developing new disease treatments. Proteins, as building blocks of biology, are a key element in all these tasks. But while it is comparatively easy to make changes to the strand of DNA that serves as the blueprint for a given protein, it remains challenging to determine which specific base pairs — rungs on the DNA ladder — are the keys to producing a desired effect. Finding these keys has been the purview of AI built of deep neural networks (DNNs), which, though effective, are notoriously opaque to human understanding.

Described in a new paper published in the Proceedings of the National Academy of Sciences, LANTERN shows the ability to predict the genetic edits needed to create useful differences in three different proteins. One is the spike-shaped protein from the surface of the SARS-CoV-2 virus that causes COVID-19; understanding how changes in the DNA can alter this spike protein might help epidemiologists predict the future of the pandemic. The other two are well-known lab workhorses: the LacI protein from the E. coli bacterium and the green fluorescent protein (GFP) used as a marker in biology experiments. Selecting these three subjects allowed the NIST team to show not only that their tool works, but also that its results are interpretable — an important characteristic for industry, which needs predictive methods that help with understanding of the underlying system.

“We have an approach that is fully interpretable and that also has no loss in predictive power,” said Peter Tonner, a statistician and computational biologist at NIST and LANTERN’s main developer. “There’s a widespread assumption that if you want one of those things you can’t have the other. We’ve shown that sometimes, you can have both.”

The problem the NIST team is tackling might be imagined as interacting with a complex machine that sports a vast control panel filled with thousands of unlabeled switches: The device is a gene, a strand of DNA that encodes a protein; the switches are base pairs on the strand. The switches all affect the device’s output somehow. If your job is to make the machine work differently in a specific way, which switches should you flip?

Because the answer might require changes to multiple base pairs, scientists have to flip some combination of them, measure the result, then choose a new combination and measure again. The number of permutations is daunting.

“The number of potential combinations can be greater than the number of atoms in the universe,” Tonner said. “You could never measure all the possibilities. It’s a ridiculously large number.”

Because of the sheer quantity of data involved, DNNs have been tasked with sorting through a sampling of data and predicting which base pairs need to be flipped. At this, they have proved successful — as long as you don’t ask for an explanation of how they get their answers. They are often described as “black boxes” because their inner workings are inscrutable.

“It is really difficult to understand how DNNs make their predictions,” said NIST physicist David Ross, one of the paper’s co-authors. “And that’s a big problem if you want to use those predictions to engineer something new.”

LANTERN, on the other hand, is explicitly designed to be understandable. Part of its explainability stems from its use of interpretable parameters to represent the data it analyzes. Rather than allowing the number of these parameters to grow extraordinarily large and often inscrutable, as is the case with DNNs, each parameter in LANTERN’s calculations has a purpose that is meant to be intuitive, helping users understand what these parameters mean and how they influence LANTERN’s predictions.

The LANTERN model represents protein mutations using vectors, widely used mathematical tools often portrayed visually as arrows. Each arrow has two properties: Its direction implies the effect of the mutation, while its length represents how strong that effect is. When two proteins have vectors that point in the same direction, LANTERN indicates that the proteins have similar function.

These vectors’ directions often map onto biological mechanisms. For example, LANTERN learned a direction associated with protein folding in all three of the datasets the team studied. (Folding plays a critical role in how a protein functions, so identifying this factor across datasets was an indication that the model functions as intended.) When making predictions, LANTERN just adds these vectors together — a method that users can trace when examining its predictions.

Other labs had already used DNNs to make predictions about what switch-flips would make useful changes to the three subject proteins, so the NIST team decided to pit LANTERN against the DNNs’ results. The new approach was not merely good enough; according to the team, it achieves a new state of the art in predictive accuracy for this type of problem.

“LANTERN equaled or outperformed nearly all alternative approaches with respect to prediction accuracy,” Tonner said. “It outperforms all other approaches in predicting changes to LacI, and it has comparable predictive accuracy for GFP for all except one. For SARS-CoV-2, it has higher predictive accuracy than all alternatives other than one type of DNN, which matched LANTERN’s accuracy but didn’t beat it.”

LANTERN figures out which sets of switches have the largest effect on a given attribute of the protein — its folding stability, for example — and summarizes how the user can tweak that attribute to achieve a desired effect. In a way, LANTERN transmutes the many switches on our machine’s panel into a few simple dials.

“It reduces thousands of switches to maybe five little dials you can turn,” Ross said. “It tells you the first dial will have a big effect, the second will have a different effect but smaller, the third even smaller, and so on. So as an engineer it tells me I can focus on the first and second dial to get the outcome I need. LANTERN lays all this out for me, and it’s incredibly helpful.”

Rajmonda Caceres, a scientist at MIT’s Lincoln Laboratory who is familiar with the method behind LANTERN, said she values the tool’s interpretability.

“There are not a lot of AI methods applied to biology applications where they explicitly design for interpretability,” said Caceres, who is not affiliated with the NIST study. “When biologists see the results, they can see what mutation is contributing to the change in the protein. This level of interpretation allows for more interdisciplinary research, because biologists can understand how the algorithm is learning and they can generate further insights about the biological system under study.”

Tonner said that while he is pleased with the results, LANTERN is not a panacea for AI’s explainability problem. Exploring alternatives to DNNs more widely would benefit the entire effort to create explainable, trustworthy AI, he said.

“In the context of predicting genetic effects on protein function, LANTERN is the first example of something that rivals DNNs in predictive power while still being fully interpretable,” Tonner said. “It provides a specific solution to a specific problem. We hope that it might apply to others, and that this work inspires the development of new interpretable approaches. We don’t want predictive AI to remain a black box.”

ALS is difficult to diagnose because, currently, there is no single test that can confirm the disease. Doctors look for neurological symptoms such as muscle weakness and upper and lower motor neuron symptoms. They will also do diagnostic tests to rule out other conditions like cervical spondylosis. A diagnostic test that could confirm ALS would help people get a diagnosis earlier and start treatment as soon as possible.

In a paper published on May 23 in JAMA Neurology, researchers outline preliminary research that could pave the way for a future test to diagnose ALS.

“It is difficult to diagnose ALS in its early stages because there is not a known biomarker,” said researcher Hirofumi Maruyama, a professor at the Graduate School of Biomedical and Health Sciences at Hiroshima University in Hiroshima, Japan. “Muscle is possible to biopsy, and transactive response DNA-binding protein 43 (TDP-43) accumulates in the peripheral nerves inside muscle. TDP-43 is a protein that plays a key role on motor neurons, and accumulation of TDP-43 may be a biomarker for early diagnosis of ALS.”

Previous research in mice has revealed a crucial function of TDP-43 in axons, the part of the neuron that sends signals to other neurons. This is important for ALS, because axonal degeneration causes the lower motor neuron problems that can be a symptom of ALS. Researchers hypothesized that TDP-43 accumulation in muscular nerve bundles could be an early predictor of ALS.

To test this theory, researchers first examined the muscle tissue of 10 individuals who had confirmed cases of ALS at the time of their death and 12 who did not. All 10 ALS patients had TDP-43 accumulations in their intramuscular nerve bundles, while 12 non-ALS controls had no TDP-43 accumulation.

Next, researchers targeted 114 patients who underwent a muscle biopsy and did not have a family history of ALS or another muscle or neuromuscular diagnosis. Of these, 71 had evidence of intramuscular nerve bundles and 43 did not. Among the 71 patients, axonal TDP-43 accumulations in their nerve bundles were confirmed in 33. These 33 patients with axonal TDP-43 accumulations were all later diagnosed with ALS. Among the 43 patients without nerve bundles, three were later diagnosed with ALS.

“Results of this dual case-control and cohort study suggest that axonal TDP-43 accumulations may be characteristic for patients with ALS, and consequently may be a novel diagnostic biomarker for ALS,” said Maruyama. “Early diagnosis enables patients to initiate prompt treatment. We aim to prevent the progression of ALS and will continue research into developing new medication.”

Story Source:

Materials provided by Hiroshima University. Note: Content may be edited for style and length.