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- Study of African animals illuminates links between environment, diet and gut microbiome
- New study suggests ‘Pac-Man-like’ mergers could explain massive, spinning black holes
- Visualizing an AI model’s blind spots
- Personalized gene networks may enhance study of disease
- The pathway to Parkinson’s takes a surprising twist
- Scientists Have Created Fake Rhino Horn. Could It Curb the Illegal Wildlife Trade?
- Genes from ‘fossil’ virus in human DNA found to be active
| Study of African animals illuminates links between environment, diet and gut microbiome
Posted: 11 Nov 2019 06:19 AM PST 
In recent years, the field of microbiome research has grown rapidly, providing newfound knowledge — and newfound questions — about the microbes that inhabit human and animal bodies. A new study adds to that foundation of knowledge by using DNA analysis to examine the relationship between diet, the environment and the microbiome. “Environmental change may influence what animals are eating, and as a consequence, influence their microbiome and health in a variety of ways that can only be understood in natural settings,” said study lead author Tyler Kartzinel, an assistant professor of ecology and evolutionary biology at Brown University and a former postdoctoral researcher at Princeton University. He added that the study’s innovative DNA-based methods might ultimately provide new avenues to study and understand human microbiomes as well. The research, published in the Proceedings of the National Academy of Sciences on Monday, Nov. 4, involved collecting and analyzing more than 1,000 samples of fecal material from 33 herbivore species — which ranged from diminutive dwarf antelopes to gigantic giraffes and elephants — in an African savanna. To build on the findings of earlier studies, the research team — a collaboration of scientists in the ecology and evolutionary biology departments at Brown and Princeton, and colleagues from the botany department of the National Museums of Kenya — sought to study a wide variety of species by analyzing samples gathered from their natural habitat. Much of the fieldwork was done at the Mpala Research Centre in Kenya, which is managed by Princeton University in partnership with the Smithsonian Institution, the National Museums of Kenya and the Kenya Wildlife Service. “A fecal sample provides an amazing window into the biology of a wild animal, from what it eats to what bacteria live in its gut to what kinds of parasites it has,” said Robert Pringle, an associate professor of ecology and evolutionary biology at Princeton and the senior author on the study. “We’re just starting to tap into the potential of what forensic DNA-based approaches to wildlife ecology can teach us about these things that have historically been very difficult if not impossible to investigate.” After analyzing the DNA in the samples to infer the animals’ diets and microbiomes, the researchers reached three main conclusions. Consistent with their expectations, they found that closely related species had similar microbiomes, and, to a lesser extent, similar diets. Their second finding was that species (and individual animals within a species) who consumed dissimilar diets tended to have dissimilar microbiomes. Lastly, the study found that animals whose diets underwent significant seasonal changes also tended to experience major seasonal changes in their microbiomes. But the team was surprised to find that the microbiomes of domesticated species such as cattle, sheep, goats, donkeys and camels tended to change more with the seasons than did the microbiomes of wild animals — even compared to the most closely related wild animals that had similar diets. Kartzinel suggested three possible factors that might explain the microbiome differences between livestock and wild animals: domestication, guidance of livestock to optimal food and water sources by human herders, and the protection of livestock in corrals at night. Together with his colleagues, he plans to explore precisely why some species’ microbiomes are more sensitive to seasonal change. Kartzinel noted that the methods employed in the study allowed for a deeper level of analysis. In the past, researchers studied microbiomes in one of two ways: Some researchers conducted “between-species” research, examining the microbiomes of a few animals representing different species — comparing, say, the microbiome of a sheep with that of a cow. Others conducted “within-species research,” comparing the microbiomes of many more animals from the same species — across, say, different seasons. However, because Kartzinel and his colleagues analyzed DNA to measure diet from individual samples collected from many different species in the same environment, they were able to conduct between-species and within-species research simultaneously. Previous between-species research tended to find that closely related species had more similar microbiomes, and within-species research found that seasons affected animals’ microbiomes. The present study adds much more nuance to these findings. “The work we’re publishing begins to bridge those findings, making the relationships seem less binary,” Kartzinel said. “Seasonal change isn’t just present or absent, for example; we found instead that there’s a gradient between the microbiomes that respond a lot and those that respond a little.” A variety of additional questions arise from the research. For example, is seasonal sensitivity in the microbiome a sign of health or a sign of trouble? “You can imagine animals changing their diets and microbiomes because they’re good at adjusting to changes in the environment,” Kartzinel said. “But you can also imagine them doing it because they’re stressed out and just trying to survive as the environment changes.” More broadly, Kartzinel and his colleagues also hope to determine which factor — diet or microbiome — tends to be more sensitive to the animal’s environment. “The same plant can provide succulent fruit for animals to eat in one season, and only offer chewy twigs in the next — if the animal eats it in both seasons, our methods wouldn’t register a change in that animal’s diet, but the animal’s gut microbiome would,” he said. He’d also like to explore experiments to determine the importance of diet and microbiome turnover for the health of wild animals. “Is the sensitivity of a herbivore’s microbiome going to help it keep a healthy diet in a changing world?” he asked. “Or do other adjustments and conditions take precedent as the animal makes decisions about how to survive? Maybe it’s a little bit of both. We’re talking about several endangered species, and the livestock people depend on, so it’s important to consider the possibilities.” If the microbiome does significantly influence animal health and behavior, Kartzinel said, then it could “affect entire food webs, communities and ecosystems, because it would determine who survives and who does not. It’s amazing to think about.” He added that in collaboration with a broader set of colleagues, the team is beginning to tap into the possibility that this research could affect humans. “The biomedical world is really interested in figuring out whether — and how — we can manage the human gut microbiome to improve health, stress and nutrition,” Kartzinel said. “Together with a whole suite of research approaches, we think these genetic methods of connecting diet and microbiome could provide an additional layer of information — for wildlife ecologists and biomedical researchers, too.” Julianna Hsing, who graduated from Princeton with a bachelor’s degree in ecology and evolutionary biology in 2016, participated in the laboratory research as part of her senior thesis. Bianca Brown, who is a Ph.D. candidate in ecology and evolutionary biology at Brown, conducted computational analyses on the microbiome. Paul Musili from the National Museums of Kenya was an additional contributor. The study was funded by the Institute at Brown for Environment and Society, the Nature Conservancy’s NatureNet Fellowship, the Princeton Environmental Institute, Princeton’s Fund for New Ideas in the Natural Sciences from the Office of the Dean for Research, the Cameron Schrier Foundation, the National Science Foundation (DEB-1355122, DEB-1457697 and IOS-1656527) and the Graduate Research Fellowship Program. By Kerry Benson for Brown University and Princeton University. |
| New study suggests ‘Pac-Man-like’ mergers could explain massive, spinning black holes
Posted: 11 Nov 2019 06:12 AM PST 
Scientists have reported detecting gravitational waves from 10 black hole mergers to date, but they are still trying to explain the origins of those mergers. The largest merger detected so far seems to have defied previous models because it has a higher spin and mass than the range thought possible. A group of researchers, including Rochester Institute of Technology Assistant Professor Richard O’Shaughnessy, has created simulations that could explain how the merger happened. In a new paper published in Physical Review Letters, the researchers suggest that such large mergers could happen just outside supermassive black holes at the center of active galactic nuclei. Gas, stars, dust and black holes become caught in a region surrounding supermassive black holes known as the accretion disk. The researchers suggest that as black holes circle around in the accretion disk, they eventually collide and merge to form a bigger black hole, which continues to devour smaller black holes, becoming increasingly large in what O’Shaughnessy calls “Pac-Man-like” behavior. “This is a very tantalizing prospect for those of us who work in this field,” said O’Shaughnessy, a member of RIT’s Center for Computational Relativity and Gravitation (CCRG). “It offers a natural way to explain high mass, high spin binary black hole mergers and to produce binaries in parts of parameter space that the other models cannot populate. There is no way to get certain types of black holes out of these other formation channels.” As the LIGO and Virgo collaboration continue to hunt for gravitational waves, O’Shaughnessy and his fellow researchers hope to find signatures of large, spinning black holes that could help confirm their models. If their assumptions are correct, it could help us better understand how the cosmic web of galaxies assembles. “This could be a unique way of probing the physics around these supermassive black holes in a way that could not be probed in any other way,” said O’Shaughnessy. “It offers unique insight into how the centers of galaxies grow, which is of course essential to understanding how galaxies as a whole grow, which explains most of the structure in the universe.” RIT’s CCRG has a large and active group of 18 faculty, students and postdoctoral researchers involved in the LIGO Scientific Collaboration. For more information, visit the CCRG website. |
| Visualizing an AI model’s blind spots
Posted: 11 Nov 2019 06:10 AM PST 
Anyone who has spent time on social media has probably noticed that GANs, or generative adversarial networks, have become remarkably good at drawing faces. They can predict what you’ll look like when you’re old and what you’d look like as a celebrity. But ask a GAN to draw scenes from the larger world and things get weird. A new demo by the MIT-IBM Watson AI Lab reveals what a model trained on scenes of churches and monuments decides to leave out when it draws its own version of, say, the Pantheon in Paris, or the Piazza di Spagna in Rome. The larger study, Seeing What a GAN Cannot Generate, was presented at the International Conference on Computer Vision last week. “Researchers typically focus on characterizing and improving what a machine-learning system can do — what it pays attention to, and how particular inputs lead to particular outputs,” says David Bau, a graduate student at MIT’s Department of Electrical Engineering and Computer Science and Computer Science and Artificial Science Laboratory (CSAIL). “With this work, we hope researchers will pay as much attention to characterizing the data that these systems ignore.” In a GAN, a pair of neural networks work together to create hyper-realistic images patterned after examples they’ve been given. Bau became interested in GANs as a way of peering inside black-box neural nets to understand the reasoning behind their decisions. An earlier tool developed with his advisor, MIT Professor Antonio Torralba, and IBM researcher Hendrik Strobelt, made it possible to identify the clusters of artificial neurons responsible for organizing the image into real-world categories like doors, trees, and clouds. A related tool, GANPaint, lets amateur artists add and remove those features from photos of their own. One day, while helping an artist use GANPaint, Bau hit on a problem. “As usual, we were chasing the numbers, trying to optimize numerical reconstruction loss to reconstruct the photo,” he says. “But my advisor has always encouraged us to look beyond the numbers and scrutinize the actual images. When we looked, the phenomenon jumped right out: People were getting dropped out selectively.” Just as GANs and other neural nets find patterns in heaps of data, they ignore patterns, too. Bau and his colleagues trained different types of GANs on indoor and outdoor scenes. But no matter where the pictures were taken, the GANs consistently omitted important details like people, cars, signs, fountains, and pieces of furniture, even when those objects appeared prominently in the image. In one GAN reconstruction, a pair of newlyweds kissing on the steps of a church are ghosted out, leaving an eerie wedding-dress texture on the cathedral door. “When GANs encounter objects they can’t generate, they seem to imagine what the scene would look like without them,” says Strobelt. “Sometimes people become bushes or disappear entirely into the building behind them.” The researchers suspect that machine laziness could be to blame; although a GAN is trained to create convincing images, it may learn it’s easier to focus on buildings and landscapes and skip harder-to-represent people and cars. Researchers have long known that GANs have a tendency to overlook some statistically meaningful details. But this may be the first study to show that state-of-the-art GANs can systematically omit entire classes of objects within an image. An AI that drops some objects from its representations may achieve its numerical goals while missing the details most important to us humans, says Bau. As engineers turn to GANs to generate synthetic images to train automated systems like self-driving cars, there’s a danger that people, signs, and other critical information could be dropped without humans realizing. It shows why model performance shouldn’t be measured by accuracy alone, says Bau. “We need to understand what the networks are and aren’t doing to make sure they are making the choices we want them to make.” Joining Bau on the study are Jun-Yan Zhu, Jonas Wulff, William Peebles, and Torralba, of MIT; Strobelt of IBM; and Bolei Zhou of the Chinese University of Hong Kong. |
| Personalized gene networks may enhance study of disease
Posted: 11 Nov 2019 06:09 AM PST  Researchers at Penn State College of Medicine have developed a new method to model how genes interact with each other — and it may someday contribute to the development of personalized treatments for patients.
According to the researchers, the new model is able to construct personalized networks for an individual patient that can show complex gene interactions in multiple directions and predict how those interactions may change over time. Genes encoded in human DNA determine physical characteristics like hair color or body shape. Historically, it was believed that a single gene influenced a single trait. Modern scientists understand that genes influence each other in a complex web of connections called gene regulatory networks. Rongling Wu, distinguished professor of public health sciences and statistics, led a team of researchers at Penn State and several other universities in developing a model that can construct gene regulatory networks for individual patients. He said that the model could help enhance the field of personalized medicine. “This model may allow us to study why patients receiving the same treatment may have different results,” said Wu, who is also a member of the Penn State Cancer Institute. “If we can identify the unique genetic processes underlying the different physical outcomes, we may be able to develop personalized treatments.” Wu described the creation and characteristics of the new model — called an idopNetwork (informative, dynamic, omnidirectional and personalized networks) — in the Oct. 11 issue of Nature Partner Journals’ Systems Biology and Applications. IdopNetworks are constructed using data obtained from genetic experiments and tests. When the genetic data are processed using differential equations, the result is a model that informs how genes relate to each other. According to the researchers, these gene relationships may differ from person to person. “There are tens of thousands of genes in human beings,” said Wu. “IdopNetworks give us the ability to reconstruct a network that paints a personal, intricate picture of the relationship between all these genes for each person.” According to Wu, groups of genes that influence each other can be organized into clusters called modules. For example, a module may show how gene A can influence gene B — whether one promotes or prevents the activity of another. It might also show how genes C, D and E influence the activity of A while genes F and G may affect the activity of gene B. Relationships between genes organized into modules can also be illustrated to show a bigger picture of gene activity in a cell, tissue or organism. “In one patient, one gene’s activity may influence a second gene’s activity,” Wu said. “It is possible that in a second patient the second gene’s activity actually influences the first gene’s activity. It is essential that we identify and understand these differences when developing personalized medicine approaches.” Wu said previous mathematical methods for constructing dynamic gene regulatory networks are limited by their necessity to collect genetic data at multiple time points. By integrating the strengths of other disciplines, such as ecology and game theory, into mathematical equations, idopNetworks do not need to rely on data from multiple time points. They can monitor the snapshots of biological processes and dynamically predict how gene networks vary in response to changes in time and environment. “Traditional approaches involved reconstructing networks at one time point from data collected at multiple time points,” said Ming Wang, co-author and professor of public health sciences at the College of Medicine. “Our approach is statistically innovative in that it allows us to use data from one time point to reconstruct a network that is dynamic and can predict changes based on time and environment.” Wu and collaborators studied genetic data collected at the University of Florida from patients who had undergone a surgical intervention for a circulatory disease in a separate study. Of the 48 participants, 35 had successful outcomes. They used the data to construct idopNetworks of 1,870 genes for each individual — and found that the people with successful outcomes had more connections within their networks. They also found that one gene played a critical role in regulating many of the genes within each person’s network. According to the researchers, once a critical gene within a network is identified, further studies can be initiated to find out how many other genes it regulates and through what methods. This data may help in designing therapeutic interventions for patients with certain conditions. It may also help scientists investigate how changes in genes contribute to human disease. “IdopNetworks are flexible and may help us build tissue-specific, gene regulatory networks using Genotype-Tissue Expression Project data,” said Chixiang Chen, first author and doctoral candidate at the College of Medicine. “That data comes from a long-term project supported by the National Institutes of Health that aims to build a comprehensive public resource containing information on gene expression in specific tissues.” Chen says idopNetworks constructed from this data set may help investigators determine what normal activity looks like in healthy tissues. It may also help them identify differences between the gene regulatory networks of healthy tissues and diseased tissues — which may help lead to the development therapeutic interventions for diseases like cancer. Biyi Shen and Zhenqiu Liu of Penn State College of Medicine contributed to this study. Libo Jiang, Beijing Forestry University; Guifang Fu, SUNY Binghamton University; Yaqun Wang, Rutgers School of Public Health; Zuoheng Wang, Yale School of Public Health; Wei Hou, Stony Brook School of Medicine; and Scott Berceli, University of Florida, also contributed to this research. This study was supported by Fundamental Research Funds for the Central Universities (Jiang) and grants from the National Institutes of Health (Wu and Berceli). |
| The pathway to Parkinson’s takes a surprising twist
Posted: 11 Nov 2019 06:08 AM PST  In neurodegenerative conditions like Parkinson’s disease, a specific group of neurons start to die one by one, causing movement problems and other symptoms. Scientists have long focused on finding out why these neurons die. Now it turns out, they might not even be dead.
Researchers at The Rockefeller University found that affected neurons in Parkinson’s disease can shut down without fully dying. These undead neurons, the team found, release chemicals that shut down their otherwise healthy neighbors as well, leading to the stuttering and halting effects seen in Parkinson’s patients. The findings, reported in October in Cell Stem Cell, suggest that future drugs aimed at halting this cell-inactivation process may help prevent the disease or slow its progression. Signs of senescence Undead cells are, in fact, pretty common. They are found all over the body. As part of a normal process called senescence, cells may shut down when they recognize they have suffered a DNA damage during division. This helps to prevent damaged cells from growing uncontrollably and causing problems like cancer. Senescence is not typically seen in the nerve cells of the brain, however. Unlike other cells in the body, neurons stop dividing once they’re fully formed. But the researchers found that, surprisingly, dopamine neurons—which regulate motivation, memory and movement by producing chemical messenger dopamine—can nevertheless become senescent. “That was a novel finding,” says Markus Riessland, the paper’s lead author. “And that was exciting for us.” The researchers, led by the late Paul Greengard, set out to investigate the exact function of a Parkinson’s-linked protein called SATB1 in dopamine-producing neurons, whose activity is reduced in Parkinson’s disease. Greengard’s lab teamed up with researchers at Memorial Sloan Kettering to grow human stem cells into dopamine neurons in a dish. In some of the neurons, they silenced the gene for SATB1. The team found that the neurons lacking SATB1 released chemicals that cause inflammation and eventually senescence in surrounding neurons. They also displayed other abnormalities, including damaged mitochondria and enlarged nuclei. None of these disruptions appeared in the dopamine neurons with intact SATB1, nor did they appear in a separate set of non-dopamine neurons lacking SATB1, meaning that the senescent pathways were specific to dopamine neurons. The team then investigated the chain of events that causes these effects following reducing SATB1. They found that SATB1 normally suppresses a gene that produces p21, a protein known to promote senescence. In other words, SATB1 appears to protect dopamine neurons from going into senescence. And when the researchers reduced SATB1 in the midbrains of mice, they found the same signs of senescence, including damaged mitochondria and high levels of p21. Brain tissue from people with Parkinson’s also showed elevated p21, further confirming the lab results. From zombies to tooth fairies The work might explain one mystery of Parkinson’s: why dopamine levels go down well before the dopamine neurons in the midbrain actually die. “They lose the function of a neuron even though they are still there,” Riessland says. “People call these senescent cells zombie cells because they’re undead, basically, and because their dead-like appearance is spreading.” The chemicals that senescent cells secrete cause local inflammation. These cells “stop the cell cycle and they start secreting inflammatory factors that signal to the immune system, ‘Come here and eat me,’” Riessland says. “This might really be a novel explanation for why you see certain markers of inflammation in Parkinson’s Disease.” The work opens up new avenues for therapies, Riessland says. There are several drugs, called senolytics, that can remove senescent cells, and the researchers suggest such drugs might help Parkinson’s patients. Another possible path is to develop new drugs to specifically target SATB1 or p21. Riessland notes that this was the last paper Greengard was working on before he died, in April. “He was excited about the work,” Riessland says. “He was joking, ‘Oh, now you’re talking about the tooth fairy, right?’ Because he was really surprised that senescence could happen in neurons.” |
| Scientists Have Created Fake Rhino Horn. Could It Curb the Illegal Wildlife Trade?
Posted: 11 Nov 2019 06:07 AM PST 
Scientists have created fake rhino horn that looks and feels so much like the real thing they argue it could help undermine the illegal market for horn, lucratively sold as an aphrodisiac in traditional Chinese medicine. The artificial horn is made out of bundled horsehair glued together with a matrix of silk, giving the material the same collagenous properties as authentic rhino horn. “The economists seem to think that if you flood the market with substitutes, the price will drop,” Fritz Vollrath, a zoologist at the University of Oxford involved in the research, told The Guardian. “If the price drops and the penalty of having rhino horn is still very high, then the value proposition changes for the trader.” Chinese demand for rhino horn has fueled an illegal international trade and put the animals at risk across Africa. Some 769 rhinos were killed by poachers in South Africa in 2018 alone. Vollrath and colleagues from Oxford worked with molecule scientists at Fudan University in Shanghai to produce the new material. They published their findings this week in the journal Scientific Reports. Most animals’ horns are made up of a bony center covered by layers of keratin. But rhino horns are actually tufts of tightly wound-hair solidified by the animal’s secretions — meaning it is made up entirely of keratin, with no bony center. Horses are close relatives of rhinos, which enabled scientists to use horsehair to create material with a similar consistency as real horn, even when cut. By flooding the market with this fake material, it could create enough doubt to curb the rhino trade, the scientists say. |
| Genes from ‘fossil’ virus in human DNA found to be active
Posted: 11 Nov 2019 06:06 AM PST  Genes from a virus that was stitched into the human genome thousands of years ago are active, producing proteins in the human brain and other tissues, according to researchers at the University of Washington School of Medicine and the Laval University School of Medicine in Quebec, Canada.
Their finding might help explain why people who inherit this “fossil virus” appear to have a higher risk of developing neurodegenerative diseases such as multiple sclerosis and Alzheimer’s. “There have been some reports that the virus, called human herpesvirus-6, can reactivate, but if it does, it’s rare,” said Dr. Alex Greninger, UW assistant professor of laboratory medicine. “What we wanted to know whether some of the virus’ individual genes were being turned on without full reactivation of the virus.”  The Journal of Virology published the article recently. Its lead authors were Vikas Peddu, a bioinformatician in the Greninger lab, and Isabelle Dubuc of Laval University. The project was led by Greninger and Louis Flamand, professor in the microbiology and immunology at Laval. The researchers were interested in two versions of human herpesvirus-6 (HHV-6) that can integrate into chromosomes and be inherited like any other human gene. HHV-6B causes the common childhood illness, roseola. This infection affects about 90 percent of children early in life, causing high fevers and rash. However, relatively little is known about the second virus, HHV-6A. After infection, both viruses can remain dormant in the body and reactivate later, particularly in people whose immune systems are suppressed. In the new study, the researchers looked at a form of the virus that is not acquired by infection but which about one in a hundred people inherit as part of their genome. About 8 percent of human DNA comes from viruses inserted into our genomes in the distant past, in many cases into the genomes of our pre-human ancestors millions of years ago. Most of these viral genes come from retroviruses, RNA viruses that insert DNA copies of their own genes into our genomes when they infect cells. HHV-6 is unique because it is the only known human DNA herpesvirus that integrates into the human genome and can be routinely inherited. HHV-6’s genome may have been accidentally copied into the human genome because it has repeating DNA sequences that resemble those found in human chromosomes. In conducting the study, the investigators analyzed a database of genome sequences of 650 people who gave consent before they died for their DNA genomes to be researched. The scientists also had access to cellular RNA in up to 40 tissue samples. Since cells must convert the instructions of active genes into RNA before they can be used to make proteins, different RNA sequences in different tissues reveal which genes are active and inactive in different cell types. “A lot of human genomicists have overlooked these integrated HHV-6 sequences in human genomes. They’re not in the human reference sequences and they’re not common enough to rise on the radar,” Greninger said. The researchers identified six individuals who had HHV-6 integrated in their genomes: two with HHV-6A and four with HHV-6B. The RNA sequences revealed that in these individuals, a number of viral genes were being actively expressed, in particular one gene called U90 and another called U100. In most tissues, the level of expression was low and sporadic, but the highest expressions were found in the esophagus, testes, adrenal gland and brain. The gene U100 codes for a viral protein that is part of the viral outer shell, or envelope. U90 codes for a protein known as a transactivator, which means it promotes the expression of other genes. Working with samples they had collected as part of another study, the Canadian researchers showed that individuals with the inherited HHV-6 genes mounted a greater immune response to viral proteins. “This suggests that even though the viral genes had been long been part of their genome, the immune systems of people who carried the genes still recognized the viral proteins as foreign,” said Flamand. “We still need to know more how the immune system gets educated by or against these endogenous viruses to understand what this increased immune response against HHV-6 proteins means.” What biological effect these proteins may be having on human cells is unknown, Greninger said. “The transactivator protein U90 is primarily responsible for turning on the viral genome. It almost as though this fossilized virus is trying to reactivate itself.” “One question we want answer is, ‘What effect does having this 150 kb viral genome present enact on expression of your human genes? We don’t know because it is present only in about 1% of the population. It will require analysis of data from very large biobanks that have associated RNA transcription sequences and the full medical records of the participants to identify which diseases these inherited HHV-6 genomes may play a role in,” he said. This work was made possible with grants from the Heart and Stroke Foundation of Canada and Canadian Institutes of Health Research. |
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