Genetics

Humans in southern Africa were an isolated population until recently

africa, Biology, evolution, Genetics, Genomics, human evolution, Science / Tim Belzer / December 3, 2025

Collectively, the genetic variants in this population are outside the range of previously described human diversity. That’s despite the fact that the present-day southern African hunter-gatherer populations are largely derived from southern African ancestors.

What’s distinct?

Estimates of the timing of when this ancient south African population branched off from any modern-day populations place the split at over 200,000 years ago, or roughly around the origin of modern humans themselves. But this wasn’t some odd, isolated group; estimates of population size based on the frequency of genetic variation suggest it was substantial.

Instead, the researchers suggest that climate and geography kept the group separate from other African populations and that southern Africa may have served as a climate refuge, providing a safe area from which modern humans could expand out to the rest of the continent when conditions were favorable. That’s consistent with the finding that some of the ancient populations in eastern and western Africa contain some southern African variants by around 5,000 years ago.

As far as genetic traits are concerned, the population looked like pretty much everyone else present at the time: brown eyes, high skin pigmentation, and no lactose tolerance. None of the older individuals had genetic resistance to malaria or sleeping sickness that are found in modern populations. In terms of changes that affect proteins, the most common are found in genes involved in immune function, a pattern that’s seen in many other human populations. More unusually, genes that affect kidney function also show a lot of variation.

So there’s nothing especially distinctive or modern apparent in this population, especially not in comparison to any other populations we know of in Africa at the same time. But they are unusual in that they suggest there was a large, stable, and isolated group from other populations present in Africa at the time. Over time, we’ll probably get additional evidence that fits this population into a coherent picture of human evolution. But for now, its presence is a bit of an enigma, given how often other populations intermingled in our past.

Nature, 2025. DOI: 10.1038/s41586-025-09811-4 (About DOIs).

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Many genes associated with dog behavior influence human personalities, too

Behavioral science, Biology, Dogs, Genetics, Genomics, golden retrievers, Science / Kris Guyer / November 26, 2025

Many dog breeds are noted for their personalities and behavioral traits, from the distinctive vocalizations of huskies to the herding of border collies. People have worked to identify the genes associated with many of these behaviors, taking advantage of the fact that dogs can interbreed. But that creates its own experimental challenges, as it can be difficult to separate some behaviors from physical traits distinctive to the breed—small dog breeds may seem more aggressive simply because they feel threatened more often.

To get around that, a team of researchers recently did the largest gene/behavior association study within a single dog breed. Taking advantage of a population of over 1,000 golden retrievers, they found a number of genes associated with behaviors within that breed. A high percentage of these genes turned out to correspond to regions of the human genome that have been associated with behavioral differences as well. But, in many cases, these associations have been with very different behaviors.

Gone to the dogs

The work, done by a team based largely at Cambridge University, utilized the Golden Retriever Lifetime Study, which involved over 3,000 owners of these dogs filling out annual surveys that included information on their dogs’ behavior. Over 1,000 of those owners also had blood samples obtained from their dogs and shipped in; the researchers used these samples to scan the dogs’ genomes for variants. Those were then compared to ratings of the dogs’ behavior on a range of issues, like fear or aggression directed toward strangers or other dogs.

Using the data, the researchers identified when different regions of the genome were frequently associated with specific variants. In total, 14 behavioral tendencies were examined, and 12 genomic regions were associated with specific behaviors, and another nine showed somewhat weaker associations. For many of these traits, it was difficult to find much because golden retrievers are notoriously friendly and mellow dogs, so they tended to score low on traits like aggression and fear.

That result was significant, as some of these same regions of the genome had been associated with very different behaviors in populations that were a mix of breeds. For example, two different regions associated with touch sensitivity in golden retrievers had been linked to a love of chasing and owner-directed aggression in a non-breed-specific study. That finding suggests that the studies were identifying genes that may be involved in setting the stage for behaviors, but were directed into specific outcomes by other genetic or environmental factors.

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Some AI tools don’t understand biology yet

AI, Biology, foundation models, gene expression, Genetics, molecular biology, Science / Kris Guyer / August 6, 2025

A collection of new studies on gene activity shows that AI tools aren’t very good.

Gene activity appears to remain beyond the abilities of AI at the moment. Credit: BSIP

Biology is an area of science where AI and machine-learning approaches have seen some spectacular successes, such as designing enzymes to digest plastics and proteins to block snake venom. But in an era of seemingly endless AI hype, it might be easy to think that we could just set AI loose on the mounds of data we’ve already generated and end up with a good understanding of most areas of biology, allowing us to skip a lot of messy experiments and the unpleasantness of research on animals.

But biology involves a whole lot more than just protein structures. And it’s extremely premature to suggest that AI can be equally effective at handling all aspects of biology. So we were intrigued to see a study comparing a set of AI software packages designed to predict how active genes will be in cells exposed to different conditions. As it turns out, the AI systems couldn’t manage to do any better than a deliberately simplified method of predicting.

The results serve as a useful caution that biology is incredibly complex, and developing AI systems that work for one aspect of it is not an indication that they can work for biology generally.

AI and gene activity

The study was conducted by a trio of researchers based in Heidelberg: Constantin Ahlmann-Eltze, Wolfgang Huber, and Simon Anders. They note that a handful of additional studies have been released while their work was on a pre-print server, all of them coming to roughly the same conclusions. But these authors’ approach is pretty easy to understand, so we’ll use it as an example.

The AI software they examined attempts to predict changes in gene activity. While every cell carries copies of the roughly 20,000 genes in the human genome, not all of them are active in a given cell—”active” in this case meaning they are producing messenger RNAs. Some provide an essential function and are active at high levels at all times. Others are only active in specific cell types, like nerves or skin. Still others are activated under specific conditions, like low oxygen or high temperatures.

Over the years, we’ve done many studies examining the activity of every gene in a given cell type under different conditions. These studies can range from using gene chips to determine which messenger RNAs are present in a population of cells to sequencing the RNAs isolated from single cells and using that data to identify which genes are active. But collectively, they can provide a broad, if incomplete, picture that links the activity of genes with different biological circumstances. It’s a picture you could potentially use to train an AI that would make predictions about gene activity under conditions that haven’t been tested.

Ahlmann-Eltze, Huber, and Anders tested a set of what are called single-cell foundation models that have been trained on this sort of gene activity data. The “single cell” portion indicates that these models have been trained on gene activity obtained from individual cells rather than a population average of a cell type. Foundation models mean that they have been trained on a broad range of data but will require additional training before they’re deployed for a specific task.

Underwhelming performance

The task in this case is predicting how gene activity might change when genes are altered. When an individual gene is lost or activated, it’s possible that the only messenger RNA that is altered is the one made by that gene. But some genes encode proteins that regulate a collection of other genes, in which case you might see changes in the activity of dozens of genes. In other cases, the loss or activation of a gene could affect a cell’s metabolism, resulting in widespread alterations of gene activity.

Things get even more complicated when two genes are involved. In many cases, the genes will do unrelated things, and you get a simple additive effect: the changes caused by the loss of one, plus the changes caused by the loss of others. But if there’s some overlap between the functions, you can get an enhancement of some changes, suppression of others, and other unexpected changes.

To start exploring these effects, researchers have intentionally altered the activity of one or more genes using the CRISPR DNA editing technology, then sequenced every RNA in the cell afterward to see what sorts of changes took place. This approach (termed Perturb-seq) is useful because it can give us a sense of what the altered gene does in a cell. But for Ahlmann-Eltze, Huber, and Anders, it provides the data they need to determine if these foundation models can be trained to predict the ensuing changes in the activity of other genes.

Starting with the foundation models, the researchers conducted additional training using data from an experiment where either one or two genes were activated using CRISPR. This training used the data from 100 individual gene activations and another 62 where two genes were activated. Then, the AI packages were asked to predict the results for another 62 pairs of genes that were activated. For comparison, the researchers also made predictions using two extremely simple models: one that always predicted that nothing would change and a second that always predicted an additive effect (meaning that activating genes A and B would produce the changes caused by activating A plus the changes caused by activating B).

They didn’t work. “All models had a prediction error substantially higher than the additive baseline,” the researchers concluded. The result held when the researchers used alternative measurements of the accuracy of the AI’s predictions.

The gist of the problem seemed to be that the trained foundation models weren’t very good at predicting when the alterations of pairs of genes would produce complex patterns of changes—when the alteration of one gene synergized with the alteration of a second. “The deep learning models rarely predicted synergistic interactions, and it was even rarer that those predictions were correct,” the researchers concluded. In a separate test that looked specifically at these synergies between genes, it turned out that none of the models were better than the simplified system that always predicted no changes.

Not there yet

The overall conclusions from the work are pretty clear. “As our deliberately simple baselines are incapable of representing realistic biological complexity yet were not outperformed by the foundation models,” the researchers write, “we conclude that the latter’s goal of providing a generalizable representation of cellular states and predicting the outcome of not-yet-performed experiments is still elusive.”

It’s important to emphasize that “still elusive” doesn’t mean we’re incapable of ever developing an AI that can help with this problem. It also doesn’t mean that this applies to all cellular states (the results are specific to gene activity), much less all of biology. At the same time, the work provides a valuable caution at a time when there’s a lot of enthusiasm for the idea that AI’s success in a couple of areas means we’re on the cusp of a world where it can be applied to anything.

Nature Methods, 2025. DOI: 10.1038/s41592-025-02772-6 (About DOIs).

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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Genetically engineered bacteria break down industrial contaminants

biochemistry, Biology, enzymes, Genetics, Genomics, pollution, Science / Tim Belzer / May 8, 2025

Once that was done, the researchers started looking through the genomes of species that have been identified as breaking down industrial contaminants. The breakdown of complex molecules typically involves more than one enzyme, and the genes for these enzymes tend to end up clustered together so they can be produced as a single, large RNA that encodes all the proteins needed. This simplifies regulating their production, making it easy to ensure the bacteria only make the proteins if the molecule they break down is actually present. In this case, the clusters ranged from just three genes all the way up to 11.

Once nine of these gene clusters were identified, the DNA that would encode them was ordered and assembled into a single DNA molecule in yeast. The researchers took some time while ordering this DNA to better optimize the genes to be active and produce proteins in Vibrio natriegens, as opposed to whatever species the genes were normally used by.

From yeast, each of these individual gene clusters was inserted into Vibrio natriegens, creating different strains that could digest one of the following: benzene, toluene, phenol, naphthalene, biphenyl, DBF29, and dibenzothiophene (DBT). (Some of the nine clusters target the same contaminant.) Each of these bacterial strains was then put in a solution with the chemical they were engineered to digest. Five of the nine worked, giving researchers strains that could digest biphenyl, phenol, napthalene, DBF, and toluene.

Good, but limited

From there, the researchers developed a system that would enable them to iteratively insert a new gene cluster at the tail end of a previously inserted gene cluster. This allowed them to build up a cluster of clusters, eventually including all five of the ones that had shown activity in the earlier tests. Given two days, this single strain could remove about a quarter of the phenol, a third of the biphenyl, 30 percent of the DBF, all of the naphthalene, and nearly all of the toluene.

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DNA links modern pueblo dwellers to Chaco Canyon people

ancient DNA, Archeology, Biology, Genetics, Genomics, human biology, Science / Mike M. / May 1, 2025

A thousand years ago, the people living in Chaco Canyon were building massive structures of intricate masonry and trading with locations as far away as Mexico. Within a century, however, the area would be largely abandoned, with little indication that the same culture was re-established elsewhere. If the people of Chaco Canyon migrated to new homes, it’s unclear where they ended up.

Around the same time that construction expanded in Chaco Canyon, far smaller pueblos began appearing in the northern Rio Grande Valley hundreds of kilometers away. These have remained occupied to the present day in New Mexico; although their populations shrank dramatically after European contact, their relationship to the Chaco culture has remained ambiguous. Until now, that is. People from one of these communities, Picuris Pueblo, worked with ancient DNA specialists to show that they are the closest relatives of the Chaco people yet discovered, confirming aspects of the pueblo’s oral traditions.

A pueblo-driven study

The list of authors of the new paper describing this genetic connection includes members of the Pueblo government, including its present governor. That’s because the study was initiated by the members of the Pueblo, who worked with archeologists to get in contact with DNA specialists at the Center for GeoGenetics at the University of Copenhagen. In a press conference, members of the Pueblo said they’d been aware of the power of DNA studies via their use in criminal cases and ancestry services. The leaders of Picuris Pueblo felt that it could help them understand their origin and the nature of some of their oral history, which linked them to the wider Pueblo-building peoples.

After two years of discussions, the collaboration settled on a plan of research, and the ancient DNA specialists were given access to both ancient skeletons at Picuris Pueblo, as well as samples from present-day residents. These were used to generate complete genome sequences.

The first clear result is that there is a strong continuity in the population living at Picuris. The ancient skeletons range from 500 to 700 years old, and thus date back to roughly the time of European contact, with some predating it. They also share strong genetic connections to the people of Chaco Canyon, where DNA has also been obtained from remains. “No other sampled population, ancient or present-day, is more closely related to Ancestral Puebloans from Pueblo Bonito [in Chaco Canyon] than the Picuris individuals are,” the paper concludes.

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In one dog breed, selection for utility may have selected for obesity

behavior, Biology, Dogs, Genetics, Labrador retriever, Science / Kris Guyer / March 14, 2025

High-risk Labradors also tended to pester their owners for food more often. Dogs with low genetic risk scores, on the other hand, stayed slim regardless of whether the owners paid attention to how and whether they were fed or not.

But other findings proved less obvious. “We’ve long known chocolate-colored Labradors are prone to being overweight, and I’ve often heard people say that’s because they’re really popular as pets for young families with toddlers that throw food on the floor all the time and where dogs are just not given that much attention,” Raffan says. Her team’s data showed that chocolate Labradors actually had a much higher genetic obesity risk than yellow or black ones

Some of the Labradors particularly prone to obesity, the study found, were guide dogs, which were included in the initial group. Training a guide dog in the UK usually takes around two years, during which the dogs learn multiple skills, like avoiding obstacles, stopping at curbs, navigating complex environments, and responding to emergency scenarios. Not all dogs are able to successfully finish this training, which is why guide dogs are often selectively bred with other guide dogs in the hope their offspring would have a better chance at making it through the same training.

But it seems that this selective breeding among guide dogs might have had unexpected consequences. “Our results raise the intriguing possibility that we may have inadvertently selected dogs prone to obesity, dogs that really like their food, because that makes them a little bit more trainable. They would do anything for a biscuit,” Raffan says.

The study also found that genes responsible for obesity in dogs are also responsible for obesity in humans. “The impact high genetic risk has on dogs leads to increased appetite. It makes them more interested in food,” Raffan claims. “Exactly the same is true in humans. If you’re at high genetic risk you aren’t inherently lazy or rubbish about overeating—it’s just you are more interested in food and get more reward from it.”

Science, 2025. DOI: 10.1126/science.ads2145

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“Wooly mice” a test run for mammoth gene editing

Biology, de-extinction, fur, gene editing, Genetics, mammoths, Mice, Science / Rejus Almole / March 5, 2025

On Tuesday, the team behind the plan to bring mammoth-like animals back to the tundra announced the creation of what it is calling wooly mice, which have long fur reminiscent of the woolly mammoth. The long fur was created through the simultaneous editing of as many as seven genes, all with a known connection to hair growth, color, and/or texture.

But don’t think that this is a sort of mouse-mammoth hybrid. Most of the genetic changes were first identified in mice, not mammoths. So, the focus is on the fact that the team could do simultaneous editing of multiple genes—something that they’ll need to be able to do to get a considerable number of mammoth-like changes into the elephant genome.

Of mice and mammoths

The team at Colossal Biosciences has started a number of de-extinction projects, including the dodo and thylacine, but its flagship project is the mammoth. In all of these cases, the plan is to take stem cells from a closely related species that has not gone extinct, and edit a series of changes based on the corresponding genomes of the deceased species. In the case of the mammoth, that means the elephant.

But the elephant poses a large number of challenges, as the draft paper that describes the new mice acknowledges. “The 22-month gestation period of elephants and their extended reproductive timeline make rapid experimental assessment impractical,” the researchers acknowledge. “Further, ethical considerations regarding the experimental manipulation of elephants, an endangered species with complex social structures and high cognitive capabilities, necessitate alternative approaches for functional testing.”

So, they turned to a species that has been used for genetic experiments for over a century: the mouse. We can do all sorts of genetic manipulations in mice, and have ways of using embryonic stem cells to get those manipulations passed on to a new generation of mice.

For testing purposes, the mouse also has a very significant advantage: mutations that change its fur are easy to spot. Over the century-plus that we’ve been using mice for research, people have noticed and observed a huge variety of mutations that affect their fur, altering color, texture, and length. In many of these cases, the changes in the DNA that cause these changes have been identified.

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These hornets break down alcohol so fast that they can’t get drunk

alcohol consumption, Biology, entymology, Genetics, hornets, insects, Science / Kris Guyer / October 29, 2024

Many animals, including humans, have developed a taste for alcohol in some form, but excessive consumption often leads to adverse health effects. One exception is the Oriental wasp. According to a new paper published in the Proceedings of the National Academy of Sciences, these wasps can guzzle seemingly unlimited amounts of ethanol regularly and at very high concentrations with no ill effects—not even intoxication. They pretty much drank honeybees used in the same experiments under the table.

“To the best of our knowledge, Oriental hornets are the only animal in nature adapted to consuming alcohol as a metabolic fuel,” said co-author Eran Levin of Tel Aviv University. “They show no signs of intoxication or illness, even after chronically consuming huge amounts of alcohol, and they eliminate it from their bodies very quickly.”

Per Levin et al., there’s a “drunken monkey” theory that predicts that certain animals well-adapted to low concentrations of ethanol in their diets nonetheless have adverse reactions at higher concentrations. Studies have shown that tree shrews, for example, can handle concentrations of up to 3.8 percent, but in laboratory conditions, when they consumed ethanol in concentrations of 10 percent or higher, they were prone to liver damage.

Similarly, fruit flies are fine with concentrations up to 4 percent but have increased mortality rates above that range. They’re certainly capable of drinking more: fruit flies can imbibe half their body volume in 15 percent (30 proof) alcohol each day. Not even spiking the ethanol with bitter quinine slows them down. Granted, they have ultra-fast metabolisms—the better to burn off the booze—but they can still become falling-down drunk. And fruit flies vary in their tolerance for alcohol depending on their genetic makeup—that is, how quickly their bodies adapt to the ethanol, requiring them to inhale more and more of it to achieve the same physical effects, much like humans.

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The fish with the genome 30 times larger than ours gets sequenced

Biology, evolution, Genetics, Genomics, lungfish, Science, tetrapods / Paul Patrick / August 14, 2024

Image of the front half of a fish, with a brown and cream pattern and long fins. — Enlarge / The African Lungfish, showing it’s thin, wispy fins.

When it was first discovered, the coelacanth caused a lot of excitement. It was a living example of a group of fish that was thought to only exist as fossils. And not just any group of fish. With their long, stalk-like fins, coelacanths and their kin are thought to include the ancestors of all vertebrates that aren’t fish—the tetrapods, or vertebrates with four limbs. Meaning, among a lot of other things, us.

Since then, however, evidence has piled up that we’re more closely related to lungfish, which live in freshwater and are found in Africa, Australia, and South America. But lungfish are a bit weird. The African and South American species have seen the limb-like fins of their ancestors reduced to thin, floppy strands. And getting some perspective on their evolutionary history has proven difficult because they have the largest genomes known in animals, with the South American lungfish genome containing over 90 billion base pairs. That’s 30 times the amount of DNA we have.

But new sequencing technology has made tackling that sort of challenge manageable, and an international collaboration has now completed the largest genome ever, one where all but one chromosome carry more DNA than is found in the human genome. The work points to a history where the South American lungfish has been adding 3 billion extra bases of DNA every 10 million years for the last 200 million years, all without adding a significant number of new genes. Instead, it seems to have lost the ability to keep junk DNA in check.

Going long

The work was enabled by a technology generically termed “long-read sequencing.” Most of the genomes that were completed were done using short reads, typically in the area of 100–200 base pairs long. The secret was to do enough sequencing that, on average, every base in the genome should be sequenced multiple times. Given that, a cleverly designed computer program could figure out where two bits of sequence overlapped and register that as a single, longer piece of sequence, repeating the process until the computer spit out long strings of contiguous bases.

The problem is that most non-microbial species have stretches of repeated sequence (think hundreds of copies of the bases G and A in a row) that were longer than a few hundred bases long—and nearly identical sequences that show up in multiple locations of the genome. These would be impossible to match to a unique location, and so the output of the genome assembly software would have lots of gaps of unknown length and sequence.

This creates extreme difficulty for genomes like that of the lungfish, which is filled with non-functional “junk” DNA, all of which is typically repetitive. The software tends to produce a genome that’s more gap than sequence.

Long-read technology gets around that by doing exactly what its name implies. Rather than being able to sequence fragments of 200 bases or so, it can generate sequences that are thousands of base pairs long, easily covering the entire repeat that would have otherwise created a gap. One early version of long-read technology involved stuffing long DNA molecules through pores and watching for different voltage changes across the pore as different bases passed through it. Another had a DNA copying enzyme make a duplicate of a long strand and watch for fluorescence changes as different bases were added. These early versions tended to be a bit error-prone but have since been improved, and several newer competing technologies are now on the market.

Back in 2021, researchers used this technology to complete the genome of the Australian lungfish—the one that maintains the limb-like fins of the ancestors that gave rise to tetrapods. Now they’re back with the genomes from African and South American species. These species seem to have gone their separate ways during the breakup of the supercontinent Gondwana, a process that started nearly 200 million years ago. And having the genomes of all three should give us some perspective on the features that are common to all lungfish species, and thus are more likely to have been shared with the distant ancestors that gave rise to tetrapods.

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Path to precision: Targeted cancer drugs go from table to trials to bedside

cancer, fda, Features, Genetics, Janet Rowley, next generation sequencing, precision medicine, Science, targeted cancer treatments, Tyrosine kinase / Paul Patrick / August 5, 2024

In 1972, Janet Rowley sat at her dining room table and cut tiny chromosomes from photographs she had taken in her laboratory. One by one, she snipped out the small figures her children teasingly called paper dolls. She then carefully laid them out in 23 matching pairs—and warned her kids not to sneeze.

The physician-scientist had just mastered a new chromosome-staining technique in a year-long sabbatical at Oxford. But it was in the dining room of her Chicago home where she made the discovery that would dramatically alter the course of cancer research.

Rowley's 1973 partial karyotype showing the 9;22 translocation — Enlarge / Rowley’s 1973 partial karyotype showing the 9;22 translocation

Looking over the chromosomes of a patient with acute myeloid leukemia (AML), she realized that segments of chromosomes 8 and 21 had broken off and swapped places—a genetic trade called a translocation. She looked at the chromosomes of other AML patients and saw the same switch: the 8;21 translocation.

Later that same year, she saw another translocation, this time in patients with a different type of blood cancer, called chronic myelogenous leukemia (CML). Patients with CML were known to carry a puzzling abnormality in chromosome 22 that made it appear shorter than normal. The abnormality was called the Philadelphia chromosome after its discovery by two researchers in Philadelphia in 1959. But it wasn’t until Rowley pored over her meticulously set dining table that it became clear why chromosome 22 was shorter—a chunk of it had broken off and traded places with a small section of chromosome 9, a 9;22 translocation.

Rowley had the first evidence that genetic abnormalities were the cause of cancer. She published her findings in 1973, with the CML translocation published in a single-author study in Nature. In the years that followed, she strongly advocated for the idea that the abnormalities were significant for cancer. But she was initially met with skepticism. At the time, many researchers considered chromosomal abnormalities to be a result of cancer, not the other way around. Rowley’s findings were rejected from the prestigious New England Journal of Medicine. “I got sort of amused tolerance at the beginning,” she said before her death in 2013.

The birth of targeted treatments

But the evidence mounted quickly. In 1977, Rowley and two of her colleagues at the University of Chicago identified another chromosomal translocation—15;17—that causes a rare blood cancer called acute promyelocytic leukemia. By 1990, over 70 translocations had been identified in cancers.

The significance mounted quickly as well. Following Rowley’s discovery of the 9;22 translocation in CML, researchers figured out that the genetic swap creates a fusion of two genes. Part of the ABL gene normally found on chromosome 9 becomes attached to the BCR gene on chromosome 22, creating the cancer-driving BCR::ABL fusion gene on chromosome 22. This genetic merger codes for a signaling protein—a tyrosine kinase—that is permanently stuck in “active” mode. As such, it perpetually triggers signaling pathways that lead white blood cells to grow uncontrollably.

Enlarge / Schematic of the 9;22 translocation and the creation of the BCR::ABL fusion gene.

By the mid-1990s, researchers had developed a drug that blocks the BCR-ABL protein, a tyrosine kinase inhibitor (TKI) called imatinib. For patients in the chronic phase of CML—about 90 percent of CML patients—imatinib raised the 10-year survival rate from less than 50 percent to a little over 80 percent. Imatinib (sold as Gleevec or Glivec) earned approval from the Food and Drug Administration in 2001, marking the first approval for a cancer therapy targeting a known genetic alteration.

With imatinib’s success, targeted cancer therapies—aka precision medicine—took off. By the early 2000s, there was widespread interest among researchers to precisely identify the genetic underpinnings of cancer. At the same time, the revolutionary development of next-generation genetic sequencing acted like jet fuel for the soaring field. The technology eased the identification of mutations and genetic abnormalities driving cancers. Sequencing is now considered standard care in the diagnosis, treatment, and management of many cancers.

The development of gene-targeting cancer therapies skyrocketed. Classes of TKIs, like imatinib, expanded particularly fast. There are now over 50 FDA-approved TKIs targeting a wide variety of cancers. For instance, the TKIs lapatinib, neratinib, tucatinib, and pyrotinib target human epidermal growth factor receptor 2 (HER2), which runs amok in some breast and gastric cancers. The TKI ruxolitinib targets Janus kinase 2, which is often mutated in the rare blood cancer myelofibrosis and the slow-growing blood cancer polycythemia vera. CML patients, meanwhile, now have five TKI therapies to choose from.

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Much of Neanderthal genetic diversity came from modern humans

Biology, evolution, Genetics, Genomics, human evolution, Neanderthals, Science / Rejus Almole / July 12, 2024

The basic outline of the interactions between modern humans and Neanderthals is now well established. The two came in contact as modern humans began their major expansion out of Africa, which occurred roughly 60,000 years ago. Humans picked up some Neanderthal DNA through interbreeding, while the Neanderthal population, always fairly small, was swept away by the waves of new arrivals.

But there are some aspects of this big-picture view that don’t entirely line up with the data. While it nicely explains the fact that Neanderthal sequences are far more common in non-African populations, it doesn’t account for the fact that every African population we’ve looked at has some DNA that matches up with Neanderthal DNA.

A study published on Thursday argues that much of this match came about because an early modern human population also left Africa and interbred with Neanderthals. But in this case, the result was to introduce modern human DNA to the Neanderthal population. The study shows that this DNA accounts for a lot of Neanderthals’ genetic diversity, suggesting that their population was even smaller than earlier estimates had suggested.

Out of Africa early

This study isn’t the first to suggest that modern humans and their genes met Neanderthals well in advance of our major out-of-Africa expansion. The key to understanding this is the genome of a Neanderthal from the Altai region of Siberia, which dates from roughly 120,000 years ago. That’s well before modern humans expanded out of Africa, yet its genome has some regions that have excellent matches to the human genome but are absent from the Denisovan lineage.

One explanation for this is that these are segments of Neanderthal DNA that were later picked up by the population that expanded out of Africa. The problem with that view is that most of these sequences also show up in African populations. So, researchers advanced the idea that an ancestral population of modern humans left Africa about 200,000 years ago, and some of its DNA was retained by Siberian Neanderthals. That’s consistent with some fossil finds that place anatomically modern humans in the Mideast at roughly the same time.

There is, however, an alternative explanation: Some of the population that expanded out of Africa 60,000 years ago and picked up Neanderthal DNA migrated back to Africa, taking the Neanderthal DNA with them. That has led to a small bit of the Neanderthal DNA persisting within African populations.

To sort this all out, a research team based at Princeton University focused on the Neanderthal DNA found in Africans, taking advantage of the fact that we now have a much larger array of completed human genomes (approximately 2,000 of them).

The work was based on a simple hypothesis. All of our work on Neanderthal DNA indicates that their population was relatively small, and thus had less genetic diversity than modern humans did. If that’s the case, then the addition of modern human DNA to the Neanderthal population should have boosted its genetic diversity. If so, then the stretches of “Neanderthal” DNA found in African populations should include some of the more diverse regions of the Neanderthal genome.

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Frozen mammoth skin retained its chromosome structure

ancient DNA, Biology, evolution, Genetics, Genomics, mammoths, Science / Mike M. / July 11, 2024

Artist's depiction of a large mammoth with brown fur and huge, curving tusks in an icy, tundra environment.

One of the challenges of working with ancient DNA samples is that damage accumulates over time, breaking up the structure of the double helix into ever smaller fragments. In the samples we’ve worked with, these fragments scatter and mix with contaminants, making reconstructing a genome a large technical challenge.

But a dramatic paper released on Thursday shows that this isn’t always true. Damage does create progressively smaller fragments of DNA over time. But, if they’re trapped in the right sort of material, they’ll stay right where they are, essentially preserving some key features of ancient chromosomes even as the underlying DNA decays. Researchers have now used that to detail the chromosome structure of mammoths, with some implications for how these mammals regulated some key genes.

DNA meets Hi-C

The backbone of DNA’s double helix consists of alternating sugars and phosphates, chemically linked together (the bases of DNA are chemically linked to these sugars). Damage from things like radiation can break these chemical linkages, with fragmentation increasing over time. When samples reach the age of something like a Neanderthal, very few fragments are longer than 100 base pairs. Since chromosomes are millions of base pairs long, it was thought that this would inevitably destroy their structure, as many of the fragments would simply diffuse away.

But that will only be true if the medium they’re in allows diffusion. And some scientists suspected that permafrost, which preserves the tissue of some now-extinct Arctic animals, might block that diffusion. So, they set out to test this using mammoth tissues, obtained from a sample termed YakInf that’s roughly 50,000 years old.

The challenge is that the molecular techniques we use to probe chromosomes take place in liquid solutions, where fragments would just drift away from each other in any case. So, the team focused on an approach termed Hi-C, which specifically preserves information about which bits of DNA were close to each other. It does this by exposing chromosomes to a chemical that will link any pieces of DNA that are close physical proximity. So, even if those pieces are fragments, they’ll be stuck to each other by the time they end up in a liquid solution.

A few enzymes are then used to convert these linked molecules to a single piece of DNA, which is then sequenced. This data, which will contain sequence information from two different parts of the genome, then tells us that those parts were once close to each other inside a cell.

Interpreting Hi-C

On its own, a single bit of data like this isn’t especially interesting; two bits of genome might end up next to each other at random. But when you have millions of bits of data like this, you can start to construct a map of how the genome is structured.

There are two basic rules governing the pattern of interactions we’d expect to see. The first is that interactions within a chromosome are going to be more common than interactions between two chromosomes. And, within a chromosome, parts that are physically closer to each other on the molecule are more likely to interact than those that are farther apart.

So, if you are looking at a specific segment of, say, chromosome 12, most of the locations Hi-C will find it interacting with will also be on chromosome 12. And the frequency of interactions will go up as you move to sequences that are ever closer to the one you’re interested in.

On its own, you can use Hi-C to help reconstruct a chromosome even if you start with nothing but fragments. But the exceptions to the expected pattern also tell us things about biology. For example, genes that are active tend to be on loops of DNA, with the two ends of the loop held together by proteins; the same is true for inactive genes. Interactions within these loops tend to be more frequent than interactions between them, subtly altering the frequency with which two fragments end up linked together during Hi-C.

Frozen mammoth skin retained its chromosome structure Read More »