A new study shows that youth arrested as juveniles with psychiatric disorders that remain untreated, struggle with mental health and successful outcomes well beyond adolescence.
Research from Northwestern Medicine shows nearly two-thirds of males and more than one-third of females with one or more existing psychiatric disorders when they entered detention, still had a disorder 15 years later.
The findings are significant because mental health struggles add to the existing racial, ethnic and economic disparities as well as academic challenges from missed school, making a successful transition to adulthood harder to attain.
“Kids get into trouble during adolescence. Those from wealthier families also use drugs and get into fights. But these situations are most often handled informally by the school and parent, and don’t culminate in arrest and detention,” said lead author Linda Teplin, Owen L. Coon Professor of psychiatry and behavioral sciences at Northwestern University Feinberg School of Medicine.
“These are not necessarily bad kids, but they have many strikes against them. Physical abuse, sexual abuse and neglect are common. These experiences can precipitate depression. Incarceration should be the last resort,” said Teplin, also a faculty associate with the University’s Institute for Policy Research.
The unprecedented longitudinal study reports on the prevalence, persistence and patterns of behavioral and psychiatric disorders in youth up to 15 years after they leave detention and whether outcomes vary by sex and race/ethnicity.

A team led by scientists at the Perelman School of Medicine at the University of Pennsylvania has illuminated the functions of mysterious structures in cells called “nuclear speckles,” showing that they can work in partnership with a key protein to enhance the activities of specific sets of genes.
The discovery, which will be published on April 5 in Molecular Cell, is an advance in basic cell biology; the key protein it identifies as a working partner of speckles is best known as major tumor-suppressor protein, p53. This avenue of research may also lead to a better future understanding of cancers, and possibly better cancer treatments.
“This study shows that nuclear speckles work as major regulators of gene expression, and suggests that they have a role in some cancers,” said study senior author Shelley Berger, PhD, the Daniel S. Och University Professor in the Department of Cell and Developmental Biology.
Nuclear speckles, tiny structures within the nucleus of every mammalian cell, were first observed with a microscope in 1910, but in the ensuing 111 years, scientists have discovered little about their functions.
One early theory was that the speckles are essentially storage depots, since they do contain important molecules needed to copy out the DNA in genes into RNA transcripts and then to process those transcripts into the finished “messenger RNAs” that can be translated into proteins. In recent years, scientists have begun to find evidence that speckles play a more direct role in gene transcription.
Nevertheless, identifying their precise functions and how those are regulated has been difficult, due to the basic challenges of studying speckles.
In the study, Berger and colleagues, including first author Katherine Alexander, PhD, a postdoctoral researcher in the Berger Laboratory who did most of the experiments, overcame some of these challenges to reveal that speckles work with p53 to directly enhance the activity of certain genes.
While p53 has long been known as a “transcription factor” or master switch that controls the activity of a broad set of genes, the researchers showed that it exerts this effect on a subset of its target genes via nuclear speckles. The protein acts as a matchmaker, bringing together speckles and DNA containing these target genes. When the speckles and genes get close, the level of transcription of the genes jumps significantly.
The researchers went even further to show that the p53 target genes whose activity is boosted via speckles have a set of functions that are broadly distinct from those of other p53 target genes.
“Speckle-associated p53 target genes, compared to other p53 target genes, are more likely to be involved in tumor-suppressing functions such as stopping cell growth and triggering cell suicide,” Alexander said.
These findings not only confirm nuclear speckles as enhancers of gene activity, but also implicate them in the functions of a key tumor-suppressor protein, which is known to be disrupted in about half of all cancers. In some cancers, p53 is mutated in a way that causes it not only to lose its tumor-suppressor function but also to actively drive cancerous growth. The researchers are now working to determine if nuclear speckles are involved in mediating this cancer-driving effect of mutant p53.
“If that proves to be the case,” Berger said, “then in principle we could develop treatments to interfere with this association between p53 and speckles — an association that might turn out to be a real Achilles heel for cancer.”

An interdisciplinary team led by KU Leuven and Stanford has identified 76 overlapping genetic locations that shape both our face and our brain. What the researchers didn’t find is evidence that this genetic overlap also predicts someone’s behavioural-cognitive traits or risk of conditions such as Alzheimer’s disease. This means that the findings help to debunk several persistent pseudoscientific claims about what our face reveals about us.
There were already indications of a genetic link between the shape of our face and that of our brain, says Professor Peter Claes from the Laboratory for Imaging Genetics at KU Leuven, who is the joint senior author of the study with Professor Joanna Wysocka from the Stanford University School of Medicine. “But our knowledge on this link was based on model organism research and clinical knowledge of extremely rare conditions,” Claes continues. “We set out to map the genetic link between individuals’ face and brain shape much more broadly, and for commonly occurring genetic variation in the larger, non-clinical population.”
Brain scans and DNA from the UK Biobank
To study genetic underpinnings of brain shape, the team applied a methodology that Peter Claes and his colleagues had already used in the past to identify genes that determine the shape of our face. Claes: “In these previous studies, we analysed 3D images of faces and linked several data points on these faces to genetic information to find correlations.” This way, the researchers were able to identify various genes that shape our face.
For the current study, the team relied on these previously acquired insights as well as the data available in the UK Biobank, a database from which they used the MRI brain scans and genetic information of 20,000 individuals. Claes: “To be able to analyse the MRI scans, we had to measure the brains shown on the scans. Our specific focus was on variations in the folded external surface of the brain — the typical ‘walnut shape’. We then went on to link the data from the image analyses to the available genetic information. This way, we identified 472 genomic locations that have an impact on the shape of our brain. 351 of these locations have never been reported before. To our surprise, we found that as many as 76 genomic locations predictive of the brain shape had previously already been found to be linked to the face shape. This makes the genetic link between face and brain shape a convincing one.”
The team also found evidence that genetic signals that influence both brain and face shape are enriched in the regions of the genome that regulate gene activity during embryogenesis, either in facial progenitor cells or in the developing brain. This makes sense, Wysocka explains, as the development of the brain and the face are coordinated. “But we did not expect that this developmental cross-talk would be so genetically complex and would have such a broad impact on human variation.”
No genetic link with behaviour or neuropsychiatric disorders
At least as important is what the researchers did not find, says Dr Sahin Naqvi from the Stanford University School of Medicine, who is the first author of this study. “We found a clear genetic link between someone’s face and their brain shape, but this overlap is almost completely unrelated to that individual’s behavioural-cognitive traits.”
Concretely: even with advanced technologies, it is impossible to predict someone’s behaviour based on their facial features. Peter Claes continues: “Our results confirm that there is no genetic evidence for a link between someone’s face and that individual’s behaviour. Therefore, we explicitly dissociate ourselves from pseudoscientific claims to the contrary. For instance, some people claim that they can detect aggressive tendencies in faces by means of artificial intelligence. Not only are such projects completely unethical, they also lack a scientific foundation.”
In their study, the authors also briefly address conditions such as Alzheimer’s, schizophrenia, and bipolar disorder. Claes: “As a starting point, we used the results that were previously published by other teams about the genetic basis of such neuropsychiatric disorders. The possible link with the genes that determine the shape of our face had never been examined before. If you compare existing findings with our new ones, you see a relatively large overlap between the genetic variants that contribute to specific neuropsychiatric disorders and those that play a role in the shape of our brain, but not for those that contribute to our face.” In other words: our risk of developing a neuropsychiatric disorder is not written on our face either.
This research is a collaboration between KU Leuven, Stanford University School of Medicine, University of Pittsburgh, Pennsylvania State University, Indiana University Purdue University Indianapolis, Cardiff University, and George Mason University.
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Approximately 40,000 children in the United States may have lost a parent to COVID-19 since February 2020, according to a statistical model created by a team of researchers. The researchers anticipate that without immediate interventions, the trauma from losing a parent could cast a shadow of mental health and economic problems well into the future for this vulnerable population.
In the researchers’ model, for approximately every 13th COVID-related death, a child loses one parent. Children who lose a parent are at higher risk of a range of problems, including traumatic prolonged grief and depression, lower educational attainment, economic insecurity and accidental death or suicide, said Ashton Verdery, associate professor of sociology, demography and social data analytics and Institute for Computational and Data Sciences co-hire, Penn State.
“When we think of COVID-19 mortality, much of the conversation focuses on the fact that older adults are the populations at greatest risk. About 81% of deaths have been among those ages 65 and older according to the CDC (Centers for Disease Control and Prevention),” said Verdery, who is also an affiliate of the Population Research Institute at Penn State. “However, that leaves 19% of deaths among those under 65 — 15% of deaths are among those in their 50s and early 60s and 3% are among those in their 40s. In these younger age groups, substantial numbers of people have children, for whom the loss of a parent is a potentially devastating challenge.”
Three-quarters of the children who lost a parent are adolescents, but one quarter are elementary-aged children, Verdery said.
The statistics of parental death are grimmer for Black families, which have been disproportionately impacted by the pandemic, according to the researchers, who report their findings in today’s (April 5) issue of JAMA Pediatrics. The team estimated that 20% of the children who lost a parent are Black even though only 14% of children in the U.S. are Black.
The model also suggests that parental deaths due to COVID-19 will increase the country’s total cases of parental bereavement by 18% to 20% over what happens in a typical year, further straining an already stretched system that does not connect all children who are eligible to adequate resources.

A new analysis of the entire genetic makeup of more than 53,000 people offers a bonanza of valuable insights into heart, lung, blood and sleep disorders, paving the way for new and better ways to treat and prevent some of the most common causes of disability and death.
The analysis from the Trans-Omics for Precision Medicine (TOPMed) program examines the complete genomes of 53,831 people of diverse backgrounds on different continents. Most are from minority groups, which have been historically underrepresented in genetic studies. The increased representation should translate into better understanding of how heart, lung, blood and sleep disorders affect minorities and should help reduce longstanding health disparities.
“The Human Genome Project has generated a lot of promises and opportunities for applying genomics to precision medicine, and the TOPMed program is a major step in this direction,” said Stephen S. Rich, PhD, a genetics researcher at the University of Virginia School of Medicine who helped lead the project. “An important feature of TOPMed is not only publishing the genomic data on 53,000 people with massive amounts of data related to heart, lung, blood and sleep disorders but also the great diversity of the participants who donated their blood and data.”
Historic Genome Analysis
The groundbreaking work identified 400 million genetic variants, of which more than 78% had never been described. Nearly 97% were extremely rare, occurring in less than 1% of people. This sheds light on both how genes mutate and on human evolution itself, the researchers say.
Of the groups studied, people of African descent had the greatest genetic variability, the researchers found. The resulting data is the best ever produced on people of African ancestry, the scientists report in the journal Nature.
The work also offers important new insights into certain gene variants that can reduce people’s ability to benefit from prescription drugs. This can vary by race and ethnic group.
“TOPMed is an important and historic effort to include under-represented minority participants in genetic studies,” said Rich, who served on the project’s Executive Committee and chaired the Steering Committee. “The work of TOPMed should translate not only into better scientific knowledge but increase diversity at all levels — scientists, trainees, participants — in work to extend personalized medicine for everyone.”
Rich was joined in the effort by UVA’s Ani Manichaikul, PhD; Joe Mychaleckyj, DPhil; and Aakrosh Ratan, PhD. All four are part of both the Center for Public Health Genomics and UVA’s Department of Public Health Sciences.
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Thousands of our daily activities, from making coffee to taking a walk to saying hello to a neighbor, are made possible through an ancient collection of brain structures tucked away near the center of the cranium.
The cluster of neurons known as the basal ganglia is a central hub for regulating a vast array of routine motor and behavior functions. But when signaling in the basal ganglia is weakened or broken, debilitating movement and psychiatric disorders can emerge, including Parkinson’s disease, Tourette’s syndrome, attention deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder.
Despite its central importance in controlling behavior, the specific, detailed paths across which information flows from the basal ganglia to other brain regions have remained poorly charted. Now, researchers at the University of California San Diego, Columbia University’s Zuckerman Institute and their colleagues have generated a precise map of brain connectivity from the largest output nucleus of the basal ganglia, an area known as the substantia nigra pars reticulata, or SNr. The findings offer a blueprint of the area’s architecture that revealed new details and a surprising level of influence connected to the basal ganglia.
The results, spearheaded by Assistant Project Scientist Lauren McElvain and carried out in the Neurophysics Laboratory of Professor David Kleinfeld at UC San Diego, and the laboratory of Zuckerman Institute Principal Investigator Rui Costa, are published April 5 in the journal Neuron.
The research establishes a new understanding of the position of the basal ganglia in the hierarchy of the motor system. According to the researchers, the newly identified pathways emerging from the connectivity map could potentially open additional avenues for intervention of Parkinson’s disease and other disorders tied to the basal ganglia.
“With the detailed circuit map in hand, we can now plan studies to identify the specific information conveyed by each pathway, how this information impacts downstream neurons to control movement and how dysfunction in each output pathway leads to the diverse symptoms of basal ganglia diseases,” said McElvain.
With support from the NIH’s Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, the researchers developed the new blueprint working in mice by applying a modern neuroscience toolset that combines techniques from genetics, virus tracing, automated microscopic imaging of whole-brain anatomy and image processing. The results revealed surprising new insights about the breadth of connections.
“These results are an example of how researchers supported by the BRAIN Initiative are using the latest brain mapping tools to change in a fundamental way our understanding of how the connections in the brain’s circuits are organized,” said John J. Ngai, director of the NIH’s BRAIN Initiative.
Previous work had emphasized that the basal ganglia architecture is dominated by a closed-loop with output projections connecting back to input structures. The new study reveals the SNr broadcasts even to lower levels of the motor and behavior system. This includes a large set of brainstem regions with direct connections to the spinal cord and motor nuclei that control muscles via a small number of intervening connections.
“The new findings led by Dr. McElvain offer an important lesson in motor control,” said Kleinfeld, a professor in the Division of Biological Sciences (Section of Neurobiology) and Division of Physical Sciences (Department of Physics). “The brain does not control movement though a hierarchy of commands, like the ‘neural networks’ of self-driving cars, but through a scheme of middle management that directs motor output while informing the executive planners.”
Remarkably, according to the researchers, the SNr neurons that project to the low levels of the motor system have branched axons that simultaneously project back up to the brain regions responsible for higher-order control and learning. In this way, the newly described connectivity of SNr neurons fundamentally links operations across high and low levels of the brain.
“The fact that specific basal ganglia output neurons project to specific downstream brain nuclei but also broadcast this information to higher motor centers has implications for how the brain chooses which movements to do in a particular context, and also for how it learns about which actions to do in the future,” said Costa, a professor of neuroscience and neurology at Columbia’s Vagelos College of Physicians and Surgeons, as well as director and chief executive officer of the Zuckerman Institute.

Researchers in the Oregon State University College of Science have taken a key step toward new drugs and vaccines for combating COVID-19 with a deep dive into one protein’s interactions with SARS-CoV-2 genetic material.
The virus’ nucleocapsid protein, or N protein, is a prime target for disease-fighting interventions because of the critical jobs it performs for the novel coronavirus’ infection cycle and because it mutates at a comparatively slow pace. Drugs and vaccines built around the work of the N protein carry the potential to be highly effective and for longer periods of time — i.e., less susceptible to resistance.
Among the SARS-CoV-2 proteins, the N protein is the viral RNA’s biggest partner. The RNA holds the genetic instructions the virus uses to get living cells, such as human cells, to make more of itself, and the N protein binds to the RNA and protects it.
Published in Biophysical Journal, the findings are an important jump-off point for additional studies of the N protein and its interactions with RNA as part of a thorough look at the mechanisms of SARS-CoV-2 infection, transmission and control.
Elisar Barbar, professor of biochemistry and biophysics at Oregon State, and Ph.D. candidate Heather Masson-Forsythe led the study with help from undergraduate students Joaquin Rodriguez and Seth Pinckney. The researchers used a range of biophysical techniques that measure changes in the size and shape of the N protein when bound to a fragment of genomic RNA — 1,000 nucleotides of the 30,000-nucleotide genome.
“The genome is rather large for a virus and requires many copies of the N protein to stick to the RNA to give the virus the spherical shape that is necessary for the virus to make more copies of itself,” Barbar said. “Our study helps us quantify how many copies of N are needed and how close they are to each other when they stick to the RNA. ”
Biophysical studies of N with large segments of RNA by nuclear magnetic resonance are rare, Barbar said, because of the difficulty of preparing the partially disordered N protein and long RNA segments, both prone to aggregation and degradation, but these kinds of studies are a specialty of the Barbar lab. Other researchers’ studies generally have been limited to much smaller pieces of RNA and smaller pieces of the N protein.
Rather than just looking at the RNA-binding regions of the N protein on their own, the 1,000-nucleotide view allowed scientists to learn that the protein binds much more strongly when it’s a full-length dimer — two copies attached to one another — and to identify regions of the protein that are essential for RNA binding.
“The full protein has structured parts but is actually really flexible, so we know that this flexibility is important for RNA binding,” Masson-Forsythe said. “We also know that as N proteins start to bind to the longer RNA, the result is a diverse collection of bound protein/RNA complexes as opposed to one way of binding.”
Drugs that thwart the N protein’s flexibility would thus be one potential avenue for pharmaceutical researchers, she said. Another possibility would be drugs that disrupt any of those protein/RNA complexes that prove to be of special significance.
A National Science Foundation Early-concept Grant for Exploratory Research (EAGER) supported this research through the NSF’s Division of Molecular and Cellular Biosciences. The Oregon State nuclear magnetic resonance facility used in the study is funded in part by the National Institutes of Health and the M.J. Murdock Charitable Trust, and the NIH also supported the native mass spectrometry data acquisition portion of the research.
Zhen Yu, Richard Cooley, Phillip Zhu and Patrick Reardon of Oregon State and James Prell and Amber Rolland of the University of Oregon were the other researchers on the project.
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Researchers at the Bloomberg~Kimmel Institute for Cancer Immunotherapy at the Johns Hopkins Kimmel Cancer Center have developed DeepTCR, a software package that employs deep-learning algorithms to analyze T-cell receptor (TCR) sequencing data. T-cell receptors are found on the surface of immune T cells. These receptors bind to certain antigens, or proteins, found on abnormal cells, such as cancer cells and cells infected with a virus or bacteria, to guide the T cells to attack and destroy the affected cells.
“DeepTCR is an open-source software that can be used to answer questions in research into infectious disease, cancer immunology and autoimmune disease; any place where the immune system has a role through its T-cell receptors,” said lead study author John-William Sidhom, an M.D./Ph.D. student at the Johns Hopkins University School of Medicine and Department of Biomedical Engineering working in the Bloomberg~Kimmel Institute for Cancer Immunotherapy.
The research was published March 11 in Nature Communications.
Sidhom was inspired to develop the software after attending a presentation on the use of deep learning for the medical sciences at the 2017 meeting of the American Association for Cancer Research. “I was doing research on T-cell receptor sequencing, and it struck me that this was the right technology to better analyze T-cell sequencing data,” he says.
Deep learning is a form of artificial intelligence that roughly mimics the workings of the human brain in terms of pattern recognition. “Deep learning is a very flexible and powerful way to do pattern recognition on any kind of data. In this paper, we use deep learning to identify patterns in sequencing data of the T-cell receptor,” says Sidhom, adding that the way the software explores T-cell receptors is analogous to an internet search. “When someone performs an internet search for an image of cats or dogs, the query doesn’t involve looking for images that have a caption that labels the image as a cat or dog, but rather applies an algorithm that explores the features of the images and recognizes patterns that identify the images as a cat or dog. This is deep learning.”
DeepTCR is a comprehensive deep-learning framework that includes both unsupervised and supervised deep learning models that can be applied at the sequence and sample level. Sidhom says the unsupervised approaches allow investigators to analyze their data in an exploratory fashion, where there may not be known immune exposures, and the supervised approaches will allow investigators to leverage known exposures to improve the learning of the models. As a result, he says, DeepTCR will enable investigators to study the function of the T-cell immune response in basic and clinical sciences by identifying the patterns in the receptors that confer the function of the T cell to recognize and kill pathological cells.
One of the main challenges of analyzing TCR sequencing data is distinguishing meaningful sequencing data from inconsequential data, and DeepTCR helps perform this analysis. “There are a lot of sequences in someone’s immune repertoire. There are a lot of pathogens that someone can be infected by, so the immune response is very broad. As a result, there is a sea of noise in the immune response, and only parts of it are important at a certain time for a certain infection,” Sidhom explains. “I may have T-cell responses to a thousand different viruses, but when the flu impacts me, I only need to utilize a small subset of those T cells to fight the flu. The main thing that the algorithm can do is isolate and match the right T cells to specific responses.”
The software package, which employs a type of deep-learning architecture called a convolutional neural network, provides users the ability to find T-cell sequencing patterns that are relevant to a specific exposure, like a flu infection, a cancer or an autoimmune disease.
“When presented with a lot of data, our algorithms can learn rules of these TCR sequence patterns. For example, we may not know the rules for how the body responds to flu, but with enough data, our software can learn those rules and then teach us what they are,” says Sidhom. “It is very well-suited to identify complex patterns in a very, very large immune repertoire to identify the interacting partners between a T-cell receptor and its antigen.”
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As the world awaits the upcoming Olympic games, a new method for detecting doping compounds in urine samples could level the playing field for those trying to keep athletics clean. Today, scientists report an approach using ion mobility-mass spectrometry to help regulatory agencies detect existing dopants and future “designer” compounds.
The researchers will present their results today at the spring meeting of the American Chemical Society (ACS).
Each year, the World Anti-Doping Agency (WADA) publishes a list of substances, including steroids, that athletes are prohibited from using. However, it can be difficult to distinguish an athlete’s natural or “endogenous” steroids from synthetic “exogenous” ones administered to boost performance.
And regulatory bodies face another challenge: “As quickly as we develop methods to look for performance-enhancing drugs, clandestine labs develop new substances that give athletes a competitive advantage,” says Christopher Chouinard, Ph.D., the project’s principal investigator. Those designer drugs evade detection if testing labs don’t know to look for their specific chemical structures.
Chouinard’s team at Florida Institute of Technology is trying to outsmart cheaters with an assay that can differentiate endogenous and exogenous steroids and can also anticipate the structure of new compounds that might show up in athletes’ urine samples.
Currently, testing labs analyze samples using tandem mass spectrometry (MS) and gas or liquid chromatography. These approaches break up molecules in the sample and separate the fragments, yielding spectra that can reveal the identity of the original, intact compounds. But it can be tough to differentiate molecules with minor structural differences — including isomers — that distinguish endogenous steroids from exogenous ones, such as the synthetic anabolic steroids athletes take to build muscle.
To accentuate those differences, Chouinard pairs MS with ion mobility (IM) spectrometry, a separation technique he learned as a graduate student with Richard Yost, Ph.D., at the University of Florida. Yost’s team and others found that the differences between isomers could be made even more apparent if the molecules in a sample were modified prior to IM-mass spec analysis by reacting them with other compounds. After Chouinard set up his own lab in 2018, he applied this technique by reacting steroid samples with ozone or acetone in the presence of ultraviolet light — reactions already well-established among researchers who study lipid isomers, but new in the anti-doping arena.
Last year, Chouinard’s team reported they had successfully used these reactions with IM-MS to improve isomer separation, identification and quantification for a few steroids in sample solutions. Now, the researchers report they have tested this technique in urine against nearly half the prohibited steroids on WADA’s list and have shown it can successfully characterize and identify these compounds. They also showed the method can characterize and identify banned glucocorticoids, such as cortisone, that improve athletic performance by suppressing inflammation from injuries. Detection limits are below one nanogram per ml.
In addition to tracking down known dopants, the team wants to be able to find newly created illicit steroids not yet known to WADA. With Florida Institute of Technology collaborators including Roberto Peverati, Ph.D., they are developing computational modeling and machine learning techniques to try to predict the structure, spectra and other characteristics of these molecules. “If we can develop methods to identify any theoretical steroids in the future, we could dramatically reduce doping because we would be able to detect these new species immediately, without the lag time that’s been associated with anti-doping testing over the last 40 years,” Chouinard says.
Though the assays themselves are quick, simple and inexpensive, IM instruments are costly, with a price ranging up to roughly a million dollars, Chouinard notes. However, he adds, with the support of anti-doping funding organizations like the Partnership for Clean Competition (PCC), more labs might be willing to foot that bill, so long as the method offers a significant advantage in detection and deterrence.
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For centuries, people in Baltic nations have used ancient amber for medicinal purposes. Even today, infants are given amber necklaces that they chew to relieve teething pain, and people put pulverized amber in elixirs and ointments for its purported anti-inflammatory and anti-infective properties. Now, scientists have pinpointed compounds that help explain Baltic amber’s therapeutic effects and that could lead to new medicines to combat antibiotic-resistant infections.
The researchers will present their results today at the spring meeting of the American Chemical Society (ACS). 
Each year in the U.S., at least 2.8 million people get antibiotic-resistant infections, leading to 35,000 deaths, according to the U.S. Centers for Disease Control and Prevention. “We knew from previous research that there were substances in Baltic amber that might lead to new antibiotics, but they had not been systematically explored,” says Elizabeth Ambrose, Ph.D., who is the principal investigator of the project. “We have now extracted and identified several compounds in Baltic amber that show activity against gram-positive, antibiotic-resistant bacteria.”
Ambrose’s interest originally stemmed from her Baltic heritage. While visiting family in Lithuania, she collected amber samples and heard stories about their medicinal uses. The Baltic Sea region contains the world’s largest deposit of the material, which is fossilized resin formed about 44 million years ago. The resin oozed from now-extinct pines in the Sciadopityaceae family and acted as a defense against microorganisms such as bacteria and fungi, as well as herbivorous insects that would become trapped in the resin.
Ambrose and graduate student Connor McDermott, who are at the University of Minnesota, analyzed commercially available Baltic amber samples, in addition to some that Ambrose had collected. “One major challenge was preparing a homogeneous fine powder from the amber pebbles that could be extracted with solvents,” McDermott explains. He used a tabletop jar rolling mill, in which the jar is filled with ceramic beads and amber pebbles and rotated on its side. Through trial and error, he determined the correct ratio of beads to pebbles to yield a semi-fine powder. Then, using various combinations of solvents and techniques, he filtered, concentrated and analyzed the amber powder extracts by gas chromatography-mass spectrometry (GC-MS).
Dozens of compounds were identified from the GC-MS spectra. The most interesting were abietic acid, dehydroabietic acid and palustric acid — 20-carbon, three-ringed organic compounds with known biological activity. Because these compounds are difficult to purify, the researchers bought pure samples and sent them to a company that tested their activity against nine bacterial species, some of which are known to be antibiotic resistant.
“The most important finding is that these compounds are active against gram-positive bacteria, such as certain Staphylococcus aureus strains, but not gram-negative bacteria,” McDermott says. Gram-positive bacteria have a less complex cell wall than gram-negative bacteria. “This implies that the composition of the bacterial membrane is important for the activity of the compounds,” he says. McDermott also obtained a Japanese umbrella pine, the closest living species to the trees that produced the resin that became Baltic amber. He extracted resin from the needles and stem and identified sclarene, a molecule present in the extracts that could theoretically undergo chemical transformations to produce the bioactive compounds the researchers found in Baltic amber samples.
“We are excited to move forward with these results,” Ambrose says. “Abietic acids and their derivatives are potentially an untapped source of new medicines, especially for treating infections caused by gram-positive bacteria, which are increasingly becoming resistant to known antibiotics.”
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