Scientists have discovered genetic clues to the cause of restless leg syndrome, a condition common among older adults. The discovery could help identify those individuals at greatest risk of the condition and point to potential ways to treat it.
Restless leg syndrome can cause an unpleasant crawling sensation in the legs and an overwhelming urge to move them. Some people experience the symptoms only occasionally, while others get symptoms every day. Symptoms are usually worse in the evening or at night-time and can severely impair sleep.
Despite the condition being relatively common — up to one in 10 older adults experience symptoms, while 2-3% are severely affected and seek medical help — little is known about its causes. People with restless leg syndrome often have other conditions, such as depression or anxiety, cardiovascular disorders, hypertension, and diabetes, but the reason why is not known.
Previous studies had identified 22 genetic risk loci — that is, regions of our genome that contain changes associated with increased risk of developing the condition. But there are still no known ‘biomarkers’ — such as genetic signatures — that could be used to objectively diagnose the condition.
To explore the condition further, an international team led by researchers at the Helmholtz Munich Institute of Neurogenomics, Institute of Human Genetics of the Technical University of Munich (TUM) and the University of Cambridge pooled and analysed data from three genome-wide association studies. These studies compared the DNA of patients and healthy controls to look for differences more commonly found in those with restless leg syndrome. By combining the data, the team was able to create a powerful dataset with more than 100,000 patients and over 1.5 million unaffected controls.
The results of the study are published today in Nature Genetics.
Co-author Dr Steven Bell from the University of Cambridge said: “This study is the largest of its kind into this common — but poorly understood — condition. By understanding the genetic basis of restless leg syndrome, we hope to find better ways to manage and treat it, potentially improving the lives of many millions of people affected worldwide.”
The team identified over 140 new genetic risk loci, increasing the number known eight-fold to 164, including three on the X chromosome. The researchers found no strong genetic differences between men and women, despite the condition being twice as common in women as it is men — this suggests that a complex interaction of genetics and the environment (including hormones) may explain the gender differences we observe in real life.

Two of the genetic differences identified by the team involve genes known as glutamate receptors 1 and 4 respectively, which are important for nerve and brain function. These could potentially be targeted by existing drugs, such as anticonvulsants like perampanel and lamotrigine, or used to develop new drugs. Early trials have already shown positive responses to these drugs in patients with restless leg syndrome.
The researchers say it would be possible to use basic information like age, sex, and genetic markers to accurately rank who is more likely to have severe restless leg syndrome in nine cases out of ten.
To understand how restless leg syndrome might affect overall health, the researchers used a technique called Mendelian randomisation. This uses genetic information to examine cause-and-effect relationships. It revealed that the syndrome increases the risk of developing diabetes.
Although low levels of iron in the blood are thought to trigger restless leg syndrome — because they can lead to a fall in the neurotransmitter dopamine — the researchers did not find strong genetic links to iron metabolism. However, they say they cannot completely rule it out as a risk factor.
Professor Juliane Winkelmann from TUM, one of senior authors of the study, said: “For the first time, we have achieved the ability to predict restless leg syndrome risk. It has been a long journey, but now we are empowered to not only treat but even prevent the onset of this condition in our patients.”

Babies’ brains are not as immature as previously thought, rather they are using the period of postnatal ‘helplessness’ to learn powerful foundation models similar to those underpinning generative Artificial Intelligence, according to a new study.
The study, led by a Trinity College Dublin neuroscientist and just published in the journal Trends in Cognitive Sciences, finds for the first time that the classic explanation for infant helplessness is not supported by modern brain data.
Compared to many animals, humans are helpless for a long time after birth. Many animals, such as horses and chickens, can walk on the day they are born. This protracted period of helplessness puts human infants at risk and places a huge burden on the parents, but surprisingly has survived evolutionary pressure.
“Since the 1960s scientists have thought that the helplessness exhibited by human babies is due to the constraints of birth. The belief was that with big heads human babies have to be born early, resulting in immature brains and a helpless period that extends up to one year of age. We wanted to find out why human babies were helpless for such a long period,” explains Professor Rhodri Cusack, Professor of Cognitive Neuroscience, and lead author of the paper.
The research team comprised Prof. Cusack, who measures development of the infant brain and mind using neuroimaging; Prof. Christine Charvet, Auburn University, USA, who compares brain development across species; and Dr. Marc’Aurelio Ranzato, a senior AI researcher at DeepMind.
“Our study compared brain development across animal species. It drew from a long-standing project, Translating Time, that equates corresponding ages across species to establish that human brains are more mature than many other species at birth,” says Prof. Charvet.
The researchers used brain imaging and found that many systems in the human infant’s brain are already functioning and processing the rich streams of information from the senses. This contradicts the long-held belief that many infant brain systems are too immature to function.

The team then compared learning in humans with the latest machine learning models, where deep neural networks benefit from a ‘helpless’ period of pre-training.
In the past, AI models were directly trained on tasks for which they were needed for example a self-driving car was trained to recognise what they see on a road. But now models are initially pre-trained to see patterns within vast quantities of data, without performing any task of importance. The resulting foundation model is subsequently used to learn specific tasks. It has been found this ultimately leads to quicker learning of new tasks and better performance.
“We propose that human infants similarly use the ‘helpless’ period in infancy to pre-train, learning powerful foundation models, which go on to underpin cognition in later life with high performance and rapid generalisation. This is very similar to the powerful machine learning models that have led to the big breakthroughs in generative AI in recent years, such as OpenAI’s ChatGPT or Google’s Gemini,” Prof. Cusack explained.
The researchers say that future research on how babies learn could well inspire the next generation of AI models.
“Although there have been big breakthroughs in AI, foundation models consume vast quantities of energy and require vastly more data than babies. Understanding how babies learn may inspire the next generation of AI models. The next steps in research would be to directly compare learning in brains and AI,” he concluded.

Eating a poor quality diet might lead to brain changes that are associated with depression and anxiety. This is according to a first-of-its-kind study into the brain chemistry and structure, and diet quality of 30 volunteers.
Brain scans show changes in neurotransmitters and grey matter volume in people who have a poor diet, versus those who adhere to a Mediterranean style diet, which is considered very healthy. The researchers also found that these changes are associated with rumination, a part of the diagnostic criteria for conditions affecting mental health, such as depression and anxiety.
This research was carried out by the University of Reading, Roehampton University, FrieslandCampina (Netherlands), and Kings College London, and is published in Nutritional Neuroscience.
When someone eats a poor quality diet, there is reduced gamma aminobutyric acid (GABA) and elevated glutamate — both neurotransmitters, along with reduced grey matter volume — in the frontal area of their brain. This could explain the association between what we eat, and how we feel.
Dr Piril Hepsomali, University of Reading, said: “We can eat ourselves well! Ultimately, we see that people who have an unhealthy diet — high in sugar and saturated fat — have imbalanced excitatory and inhibitory neurotransmission, as well as reduced volume of grey matter in the frontal part of the brain. This part of the brain is involved in mental health issues such as depression and anxiety.”
The exact reason that diet affects the brain in this way is still under investigation. It’s possible that obesity and dietary patterns that are high in saturated fats cause changes in glutamate and GABA metabolism and neurotransmission, as has been shown in animal studies.
Distinct alterations of the gut microbiome, due to dietary patterns that are high in saturated fats, is thought to have an impact on cell machinery that drives both GABA and glutamate production.

A high saturated fat, high sugar, diet has also been shown to reduce the number of parvalbumin interneurons, which perform the role of delivering GABA to where it is needed.
Unhealthy diets also have an impact on glucose, making blood glucose and insulin higher. This increases glutamate in the brain and plasma, thus reducing GABA production and release. Having a diet high in fat and cholesterol can cause changes in cell membranes that alter the release of neurotransmitters, too.
These changes in brain chemistry might lead to changes in the brain grey matter volume, as observed in this study.
Dr Hepsomali continued: “I would like to note that GABA and glutamate are intimately involved in appetite and food intake, too. Reduced GABA and/or increased glutamate might also be a driving factor in making unhealthy food choices. So, there may be a circular relationship between eating well, having a healthier brain and better mental wellbeing, and making better food choices to eat well.”

Human skin, hair and nails are all vulnerable to fungal infections. While these infections are usually not serious, they’re difficult to fully resolve and often recur after treatment — sometimes for years. They’re also often resistant to treatments, including a common class of antifungals called azoles.
A study published this week in mSphere points to a new way to boost the efficacy of azoles in treating those infections — combining them with commonly available drugs called bisphosphonates, usually used to treat osteoporosis. Previous work by the same research group has shown that adding bisphosphonates to azoles can effectively treat yeast infections from Candida and Cryptococcus species. The new study extends that finding to dermatophytes, which cause superficial infections.
The study suggests that bisphosphonates could be repurposed to use with azole antifungals, which are relatively nontoxic, and that the combination could be readily tested in clinical studies.
“There aren’t many good antifungal drugs around, and the fungus will always develop resistance no matter how you treat them,” said senior author Dee Carter, a mycologist at the University of New South Wales, Australia. “For what we’re proposing, to use 2 drugs at the same time, that resistance is much less likely.”
Carter says she was only a little surprised to see the synergistic effect in dermatophytes. This research project began years ago, she says, when her group conducted a series of genomic analyses on the response of pathogenic yeasts to drug treatment. That study led them to a genetic pathway that is “upstream” of the pathway targeted by azoles, and is instead targeted by bisphosphonates. “That’s often a good way to get drugs to work, is to target processes that are interlinked like that,” Carter said.
In the lab, Carter and her team tested 3 commonly available bisphosphonates (risedronate, alendronate and zoledronate) in combination with 3 commonly available azole antifungals (fluconazole, itraconazole and ketoconazole). They tested these against clinical isolates from a diverse range of dermatophyte species that cause superficial infections. The combination of zoledronate and ketoconazole proved particularly effective against 8 of the 9 tested species. It also prevented the development of resistance.
Aidan Kane, mycologist, lead author of the study and a Ph.D. student in Carter’s lab, used fluorescence microscopy and other methods to show that the drug combination worked by weakening the cell membrane enough that the fungus could not survive. Beyond the dermatophytes, the group also found that bisphosphonate-azole combinations could act against molds that cause invasive disease, suggesting another possible clinical application for the drugs.
“You can use these combinations directly for superficial infections, like Candida or dermatophyte infections, and we’re hoping that with modified forms of the drugs we can create something that will also work systematically,” Carter said. “The next step is to continue testing bisphosphonate-azole combinations in animal models, and then test it in clinical trials.”

A team of researchers from the Case Western Reserve University School of Medicine has developed a new method for target DNA sequence amplification, testing and analysis.
This new technique, or reaction, known as AMPLON (Amplifying DNA with Multiarm Priming and Looping Optimization of Nucleic Acid), offers an alternative to the previously accepted “gold-standard” Polymerase Chain Reaction (PCR) method, opening the opportunity for more applications in medical diagnosis.
The team’s findings were recently published in the journal Advanced Materials.
“AMPLON has the potential to positively change the way molecular analysis and clinical diagnostics are performed,” said Mohamed S. Draz, an assistant professor at the School of Medicine and the study’s principal investigator, “from infectious-disease diagnostics to personalized medicine and environmental monitoring.”
How it works
Researchers use such technology to compare the DNA of sick cells to that of healthy cells, allowing them to better understand the changes that occur as a disease progresses and how to treat it.
AMPLON provides several extensions along the DNA strand to simultaneously increase the speed and accuracy of DNA synthesis under constant temperature conditions.

Using this new simplified process eliminates the need to operate between high and low temperature extremes that can cause stress on materials. It also makes the amplification process more structured and accessible, especially in settings where precise temperature control is challenging.
Using the traditional PCR method, the DNA sample is heated so it can separate into two pieces of single-stranded DNA. Next, an enzyme builds two new strands of DNA, using the original strands as templates. The process is tedious, time consuming and expensive.
“We’ve developed a new method of DNA amplification that does not require bulky lab-bound equipment but can be conducted in one step and in diverse settings,” Draz said. “More significantly, our approach does not weaken enzymes like the PCR method.”
AMPLON’s multiarmed DNA primer design can turn the shortcomings of enzymes into strengths to improve amplification efficiency and produce consistent results.
“We’ve been able to enhance amplification and reduce amplification time by 50%,” Draz said. “Our approach has the potential to dramatically change the way nucleic acid amplification is performed, providing instead a portable, reliable and cost-effective solution for applications, ranging from point-of-care diagnostics to field-based research.” said Draz.

Adolescents with elevated blood pressure and arterial stiffness may experience poorer cognitive functions, according to a recent Finnish study conducted at the University of Jyväskylä and the University of Eastern Finland. Young people with higher blood pressure performed worse, especially in tasks that measured attention and learning. In addition, arterial stiffness was reflected in weaker working memory. In view of the findings, the importance of preventing high blood pressure and arterial stiffness in childhood and adolescence is emphasized.
It is well-established that poor arterial health can lead to cognitive decline in adults. However, there is limited knowledge about this connection in adolescents. To address this gap, this study examined the associations of arterial stiffness and blood pressure with cognition in adolescents and whether these associations differed between girls and boys. Moreover, it examined whether physical activity or sedentary time are confounding factors in these associations.
Higher blood pressure was a more significant factor in the brain health of girls
Adolescents with higher blood pressure had poorer attention, learning, and overall cognition. Higher pulse wave velocity, an indicator of arterial stiffness, was associated with poorer working memory.
Interestingly, girls with higher blood pressure demonstrated a negative association with a broader range of cognitive functions than boys. Conversely, boys with higher arterial stiffness exhibited better attention and working memory. The associations were not influenced by either physical activity or sedentary time.
“Our findings underscore the importance of preventing high blood pressure and arterial stiffening to promote cognitive and brain health in young people. However, we did observe some contradictory associations,” says Doctoral Researcher Petri Jalanko from the Faculty of Sport and Health Sciences at the University of Jyväskylä.
“The study provides insight into how blood pressure and arterial stiffness are linked to cognitive function. However, to establish a definitive cause-and-effect relationship between arterial health and brain health, and to determine whether increasing physical activity or reducing sedentary time can mitigate the negative effects of poor arterial health on cognition, further randomized controlled trials with appropriate control groups and advanced brain imaging techniques are necessary.”
The study utilized cross-sectional data from the eight-year follow-up assessments of the Physical Activity and Nutrition in Children (PANIC) study. A total of 116 adolescents (45 girls and 71 boys) participated, and their mean age was 15.9 years. Systolic and diastolic blood pressure were measured using an aneroid sphygmomanometer. Pulse wave velocity was measured by impedance cardiography, while carotid intima-media thickness and carotid artery distensibility were measured by carotid ultrasonography. The CogState test battery was used to assess cognition, with overall cognition computed from the results of attention, working memory, and learning tests. Physical activity and sedentary time were assessed using a combined accelerometer/heart rate monitor. The study was published in Physiological Reports.

Calcific aortic valve disease (CAVD) is the major heart valve disease that afflicts nearly 10 million patients globally with an annual mortality exceeding 100,000, and the numbers continue to rise. In CAVD, microcrystals of hydroxyapatite (a calcium phosphate mineral) deposit onto the heart valve leaflets and impair cardiac function. The disease has a dismal prognosis with most untreated patients dying two years after diagnosis. Currently, the only available treatment is surgical aortic valve replacement, which is not appropriate for all patients.
While previous studies of the histology samples from explanted calcified aortic valves have found amyloid deposits in or near calcified areas, the causal relationship between amyloid deposition and calcification is unclear.
Researchers from Boston University Chobanian & Avedisian School of Medicine, in collaboration with clinical cardiologists from New York University and University of Texas Houston, have now proposed a molecular mechanism that links amyloid deposition in the aortic valve with degenerative calcification. They also theorize that other risk factors for CAVD, such as high blood levels of lipoprotein, can contribute to calcification both directly and indirectly through the mechanisms that involve amyloid accumulation.
Harnessing the “resolution revolution” in cryogenic electron microscopy, groups of researchers around the world were able to determine hundreds of structures of patient-derived protein aggregates called amyloid fibrils. Such fibrils are associated with major human diseases including Alzheimer’s and Parkinson’s diseases, diabetes, and heart diseases such as atherosclerosis and calcific aortic valve disease. “We noticed that the unique geometry of amyloid fibrils, with their periodic arrays of acidic residues on the surface, provides a perfect match for the precursors of calcium phosphate crystals that deposit in the heart valve and impair its normal function,” explained corresponding author Olga Gursky, PhD, professor of pharmacology, physiology & biophysics at the school.
The researchers propose that amyloid deposits, which are often found adjacent to calcified areas of the heart valve, can modulate CAVD and accelerate degenerative calcification. This implies that blocking amyloid formation may be a much-needed novel therapeutic target for CAVD and, potentially, other diseases involving degenerative calcification and amyloid formation, such as Alzheimer’ disease.
“We hope that a better understanding of molecular mechanisms and drivers of degenerative biomineralization will help identify new therapeutic targets for major human diseases, such as calcific aortic valve disease,” adds Gursky.

An international research team has found almost a million potential sources of antibiotics in the natural world.
Research published in the journal Cell by a team including Queensland University of Technology (QUT) computational biologist Associate Professor Luis Pedro Coelho has used machine learning to identify 863,498 promising antimicrobial peptides — small molecules that can kill or inhibit the growth of infectious microbes.
The findings of the study come with a renewed global focus on combatting antimicrobial resistance (AMR) as humanity contends with the growing number of superbugs resistant to current drugs.
“There is an urgent need for new methods for antibiotic discovery,” Professor Coelho, a researcher at the QUT Centre for Microbiome Research, said. The centre studies the structure and function of microbial communities from around the globe.
“It is one of the top public health threats, killing 1.27 million people each year.”
Without intervention, it is estimated that AMR could cause up to 10 million deaths per year by 2050.
“Using artificial intelligence to understand and harness the power of the global microbiome will hopefully drive innovative research for better public health outcomes,” he said.

The team verified the machine predictions by testing 100 laboratory-made peptides against clinically significant pathogens. They found 79 disrupted bacterial membranes and 63 specifically targeted antibiotic-resistant bacteria such as Staphylococcus aureus and Escherichia coli.
“Moreover, some peptides helped to eliminate infections in mice; two in particular reduced bacteria by up to four orders of magnitude,” Professor Coelho said.
In a preclinical model, tested on infected mice, treatment with these peptides produced results similar to the effects of polymyxin B — a commercially available antibiotic which is used to treat meningitis, pneumonia, sepsis and urinary tract infections.
More than 60,000 metagenomes (a collection of genomes within a specific environment), which together contained the genetic makeup of over one million organisms, were analysed to get these results. They came from sources across the globe including marine and soil environments, and human and animal guts.
The resulting AMPSphere — a comprehensive database comprising these novel peptides — has been published as a publicly available, open-access resource for new antibiotic discovery.
Professor Coelho’s research was conducted as part of his ARC Future Fellowship through the QUT School of Biomedical Science, in collaboration with the Cesar de la Fuente laboratory at the University of Pennsylvania, Fudan University, the European Molecular Biology Laboratory and APC Microbiome Ireland.

Almost a century ago, the discovery of antibiotics like penicillin revolutionized medicine by harnessing the natural bacteria-killing abilities of microbes. Today, a new study co-led by researchers at the Perelman School of Medicine at the University of Pennsylvania suggests that natural-product antibiotic discovery is about to accelerate into a new era, powered by artificial intelligence (AI).
The study, published in Cell, details how the researchers used a form of AI called machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This unprecedented effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.
“AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers” said study co-senior author César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering.
Nature has always been a good place to look for new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins (“peptides”) that can disrupt bacterial cell membranes and other critical structures. While the discovery of penicillin and other natural-product-derived antibiotics revolutionized medicine, the growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.
In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.
For this new study, the research team used a machine learning platform to sift through multiple public databases containing microbial genomic data. The analysis covered 87,920 genomes from specific microbes as well as 63,410 mixes of microbial genomes — “metagenomes” — from environmental samples. This comprehensive exploration spanned diverse habitats around the planet.
This extensive exploration succeeded in identifying 863,498 candidate antimicrobial peptides, more than 90 percent of which had never been described before. To validate these findings, the researchers synthesized 100 of these peptides and tested them against 11 disease-causing bacterial strains, including antibiotic-resistant strains of E. coli and Staphylococcus aureus.

“Our initial screening revealed that 63 of these 100 candidates completely eradicated the growth of at least one of the pathogens tested, and often multiple strains,” de la Fuente said. “In some cases, these molecules were effective against bacteria at very low doses.”
Promising results were also observed in preclinical animal models, where some of the potent compounds successfully stopped infections. Further analysis suggested that many of these candidate molecules destroy bacteria by disrupting their outer protective membranes, effectively popping them like balloons.
The identified compounds originated from microbes living in a wide variety of habitats, including human saliva, pig guts, soil and plants, corals, and many other terrestrial and marine organisms. This validates the researchers’ broad approach to exploring the world’s biological data.
Overall, the findings demonstrate the power of AI in discovering new antibiotics, providing multiple new leads for antibiotic developers, and signaling the start of a promising new era in antibiotic discovery.
The team has published their repository of putative antimicrobial sequences, which they call AMPSphere, which is open access and freely available at https://ampsphere.big-data-biology.org/

The human genome contains about 23,000 genes, but only a fraction of those genes are turned on inside a cell at any given time. The complex network of regulatory elements that controls gene expression includes regions of the genome called enhancers, which are often located far from the genes that they regulate.
This distance can make it difficult to map the complex interactions between genes and enhancers. To overcome that, MIT researchers have invented a new technique that allows them to observe the timing of gene and enhancer activation in a cell. When a gene is turned on around the same time as a particular enhancer, it strongly suggests the enhancer is controlling that gene.
Learning more about which enhancers control which genes, in different types of cells, could help researchers identify potential drug targets for genetic disorders. Genomic studies have identified mutations in many non-protein-coding regions that are linked to a variety of diseases. Could these be unknown enhancers?
“When people start using genetic technology to identify regions of chromosomes that have disease information, most of those sites don’t correspond to genes. We suspect they correspond to these enhancers, which can be quite distant from a promoter, so it’s very important to be able to identify these enhancers,” says Phillip Sharp, an MIT Institute Professor Emeritus and member of MIT’s Koch Institute for Integrative Cancer Research.
Sharp is the senior author of the new study, which will appear in Nature. MIT Research Assistant D.B. Jay Mahat is the lead author of the paper.
Hunting for eRNA
Less than 2 percent of the human genome consists of protein-coding genes. The rest of the genome includes many elements that control when and how those genes are expressed. Enhancers, which are thought to turn genes on by coming into physical contact with gene promoter regions through transiently forming a complex, were discovered about 45 years ago.

More recently, in 2010, researchers discovered that these enhancers are transcribed into RNA molecules, known as enhancer RNA or eRNA. Scientists suspect that this transcription occurs when the enhancers are actively interacting with their target genes. This raised the possibility that measuring eRNA transcription levels could help researchers determine when an enhancer is active, as well as which genes it’s targeting.
“That information is extraordinarily important in understanding how development occurs, and in understanding how cancers change their regulatory programs and activate processes that lead to de-differentiation and metastatic growth,” Mahat says.
However, this kind of mapping has proven difficult to perform because eRNA is produced in very small quantities and does not last long in the cell. Additionally, eRNA lacks a modification known as a poly-A tail, which is the “hook” that most techniques use to pull RNA out of a cell.
One way to capture eRNA is to add a nucleotide to cells that halts transcription when incorporated into RNA. These nucleotides also contain a tag called biotin that can be used to fish the RNA out of a cell. However, this current technique only works on large pools of cells and doesn’t give information about individual cells.
While brainstorming ideas for new ways to capture eRNA, Mahat and Sharp considered using click chemistry, a technique that can be used to join two molecules together if they are each tagged with “click handles” that can react together.
The researchers designed nucleotides labeled with one click handle, and once these nucleotides are incorporated into growing eRNA strands, the strands can be fished out with a tag containing the complementary handle. This allowed the researchers to capture eRNA and then purify, amplify, and sequence it. Some RNA is lost at each step, but Mahat estimates that they can successfully pull out about 10 percent of the eRNA from a given cell.

Using this technique, the researchers obtained a snapshot of the enhancers and genes that are being actively transcribed at a given time in a cell.
“You want to be able to determine, in every cell, the activation of transcription from regulatory elements and from their corresponding gene. And this has to be done in a single cell because that’s where you can detect synchrony or asynchrony between regulatory elements and genes,” Mahat says.
Timing of gene expression
Demonstrating their technique in mouse embryonic stem cells, the researchers found that they could calculate approximately when a particular region starts to be transcribed, based on the length of the RNA strand and the speed of the polymerase (the enzyme responsible for transcription) — that is, how far the polymerase transcribes per second. This allowed them to determine which genes and enhancers were being transcribed around the same time.
The researchers used this approach to determine the timing of the expression of cell cycle genes in more detail than has previously been possible. They were also able to confirm several sets of known gene-enhancer pairs and generated a list of about 50,000 possible enhancer-gene pairs that they can now try to verify.
Learning which enhancers control which genes would prove valuable in developing new treatments for diseases with a genetic basis. Last year, the U.S. Food and Drug Administration approved the first gene therapy treatment for sickle cell anemia, which works by interfering with an enhancer that results in activation of a fetal globin gene, reducing the production of sickled blood cells.
The MIT team is now applying this approach to other types of cells, with a focus on autoimmune diseases. Working with researchers at Boston Children’s Hospital, they are exploring immune cell mutations that have been linked to lupus, many of which are found in non-coding regions of the genome.
“It’s not clear which genes are affected by these mutations, so we are beginning to tease apart the genes these putative enhancers might be regulating, and in what cell types these enhancers are active,” Mahat says. “This is a tool for creating gene-to-enhancer maps, which are fundamental in understanding the biology, and also a foundation for understanding disease.”
The findings of this study also offer evidence for a theory that Sharp has recently developed, along with MIT professors Richard Young and Arup Chakraborty, that gene transcription is controlled by membraneless droplets known as condensates. These condensates are made of large clusters of enzymes and RNA, which Sharp suggests may include eRNA produced at enhancer sites.
“We picture that the communication between an enhancer and a promoter is a condensate-type, transient structure, and RNA is part of that. This is an important piece of work in building the understanding of how RNAs from enhancers could be active,” he says.