Won, assistant professor of genetics and member of the UNC Neuroscience Center, and colleagues identified genes linked to cigarette smoking and drinking. The researchers found that these genes are over-represented in certain kinds of neurons — brain cells that trigger other cells to send chemical signals throughout the brain.

The researchers, who published their work in the journal Molecular Psychiatry, also found that the genes underlying cigarette smoking were linked to the perception of pain and response to food, as well as the abuse of other drugs, such as cocaine. Other genes associated with alcohol use were linked to stress and learning, as well as abuse of other drugs, such as morphine.

Given the lack of current treatment options for substance use disorder, the researchers also conducted analyses of a publicly available drug database to identify potential new treatments for substance abuse.

“We found that antipsychotics and other mood stabilizers could potentially provide therapeutic relief for individuals struggling with substance abuse,” said Nancy Sey, graduate student in the Won lab and the first author of the paper. “And we’re confident our research provides a good foundation for research focused on creating better treatments to address drug dependency.”

Parsing the Genome

Long-term substance use and substance use disorders have been linked to many common diseases and conditions, such as lung cancer, liver disease, and mental illnesses. Yet, few treatment options are available, largely due to gaps in our understanding of the biological processes involved.

“We know from twin studies that genetics may account for why some people use and abuse substances, aside from environmental factors, such as family issues or personal trauma,” Won said. “Genetic studies such as genome-wide association studies (GWAS) provide a way to identify genes associated with complex human traits, such as nicotine addiction or drinking heavily.”

Through GWAS, Won added, researchers can identify regions in the genome that play roles in particular traits, compared to individuals who do not exhibit the trait. Yet, genome-wide studies cannot tell us much about how genes in those regions affect a trait. That’s because these regions are often in “non-coding” regions of the genome.

“Non-coding” refers to the fact that the genes in these regions do not translate — or code — their genetic information directly into the creation of proteins, which then perform a known biological function. Therefore, what actually happens biologically in these “non-coding” regions remains mostly unknown.

“We wanted to learn what’s happening in these regions,” Won said. “So we developed Hi-C coupled MAGMA (H-MAGMA), a computational tool to help us make more sense of what we’re seeing in genome-wide studies.”

In a previous publication, Won’s lab showed how applying H-MAGMA to brain disorders identifies their associated genes and described their underlying biology. And for this current paper, her lab expanded the tool to cigarette smoking and drinking.

They developed H-MAGMA frameworks from dopaminergic neurons and cortical neurons — brain cell types that researchers have long implicated in substance use. Focusing on those two cell types, Won’s team — led by Sey, an HHMI Gilliam Fellow — applied H-MAGMA to GWAS findings related to heaviness of smoking, nicotine dependence, problematic alcohol use, and heaviness of drinking to identify genes associated with each trait.

Genes associated with alcohol use and cigarette smoking were also associated with other types of substances, such as morphine and cocaine. While the opioid crisis has caused a detrimental social burden, well-powered GWAS on cocaine and opioid use are not currently available. Won’s team, therefore, sought to determine whether the genes associated with alcohol use and cigarette smoking can reveal genetics underlying general addiction behavior, genetic findings that could be extended to other substances of abuse.

“Our analyses showed that expression of genes shared between cigarette smoking and alcohol use traits can be altered by other types of substances such as cocaine,” Won said. “By characterizing the biological function of these genes, we will be able to identify the biological mechanisms underlying addiction, which could be generalized to various forms of substance use disorder.”

In addition to the various types of excitatory neurons, Won’s team also identified additional cell types, including cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons that are associated with the risk genes.

With these findings in hand, it is now possible for the UNC researchers and others to investigate molecules that make addiction much less likely.

This research was supported through grants from the National Institute on Drug Abuse (R21DA051921, U01DA048279); the National Institute of Mental Health (R00MH113823, DP2MH122403); the NARSAD Young Investigator Award from the Brain and Behavior Research Foundation; the National Science Foundation Graduate Research Fellowship Program (DGE-1650116); the University of North Carolina at Chapel Hill; the Howard Hughes Medical Institute’s James H. Gilliam Fellowship for Advanced Study Program; and the National Institute of General Medical Sciences (5T32GM067553).

The research team found that, even in regions that keep receiving about the same amount of precipitation, streamflow will become more variable and unpredictable. As snowpack recedes in a warmer future and fails to provide reliable runoff, the amount and timing of water resources will become increasingly reliant on periodic episodes of rain.

“Water managers will be at the whim of individual precipitation events instead of having four-to-six months lead time to anticipate snowmelt and runoff,” said NCAR scientist Will Wieder, the lead author. “Water management systems in snow-dominated regions are based on the predictability of snowpack and runoff, and much of that predictability could go away with climate change.”

Observations show that snowpack is already melting earlier, and even declining in many regions. This decline will become so pronounced toward the end of the century that the amount of water contained in snowpack at the end of an average winter in parts of the U.S. Rocky Mountains could plummet by nearly 80 percent, the scientists found.

The changes in runoff and streamflow are likely to have cascading impacts on ecosystems that depend on reliable water from snow, the study warns. Although the changes won’t be uniform across regions, more snow-free days and longer growing seasons will put stress on water resources, drying out soils in many areas and heightening fire risk.

The study assumes that emissions of greenhouse gasses continue at a high rate (a scenario known as SSP3-7.0). Wieder said that the most severe impacts on snowpack, runoff, and ecosystems would likely be avoided if society successfully reduced greenhouse gas emissions.

The scientists drew on an advanced set of computer simulations to fill in details about the future of water resources, showing the extent to which changes in temperature and precipitation will alter snow accumulation and runoff patterns in the Northern Hemisphere. Although past research looked at the impacts of climate change on water availability, the new study focuses on the increasing variability of water resources.

The study is being published the week of July 18 in the Proceedings of the National Academy of Sciences. It was funded by the U.S. National Science Foundation, which is NCAR’s sponsor.

A race with predictability

Many regions of Earth rely on the accumulation of snow during the winter and subsequent melting in the spring and summer for regulating runoff and streamflow. For years, however, scientists have warned that the snowpack will become thinner and melt earlier as more precipitation during the colder months falls as rain instead of snow, and as melting occurs at times during the winter instead of the spring runoff season.

To determine how reduced snowpack will affect the variability of water resources, Wieder and his co-authors turned to a powerful NCAR-based climate model: the Community Earth System Model, version 2. They drew on a recently created database of simulations, known as the CESM2 Large Ensemble, to compare a past period (1940-1969) with a future period (2070-2099). The simulations were run on the Aleph supercomputer at the Institute for Basic Science supercomputer in Busan, South Korea.

The results illuminate the extent to which widespread shifts in the timing and extent of water flows will occur in much of the world by 2100. There will be an average of about 45 more snow-free days yearly in the Northern Hemisphere, assuming high greenhouse gas emissions. The largest increases will occur in midlatitudes that are relatively warm and high-latitude maritime regions that are influenced by changes in sea ice.

Many regions that rely the most on predictable relationships between snowpack and runoff will experience the largest loss in predictability because of a sharp decline in reliable pulses of spring runoff. These regions include the Rocky Mountains, Canadian Arctic, Eastern North America, and Eastern Europe. The authors warn that this will substantially complicate the management of freshwater resources, both for society and ecosystems.

“We are in a race with predictability when it comes to streamflow because we’re trying to improve our forecasts through better data, models, and physical understanding, but these efforts are being canceled by the rapid disappearance of our best predictor: snow,” said Flavio Lehner, a professor of earth and atmospheric science at Cornell University and a co-author of the study. “It might be a race we’ll lose, but we’re trying to win it, and that is why we need to study these topics.”

Although the reduced runoff will result in drier summertime soil conditions in much of the Northern Hemisphere, the simulations showed that certain regions — including East Asia, the Himalayas, and Northwestern North America — will maintain soil moisture because of increased rainfall.

“Snow-related metrics are critical for informing society’s management of precious water resources,” said Keith Musselman, a hydrologist at the University of Colorado Boulder and co-author of the study. “As utilities and civil works agencies plan new reservoirs and other infrastructure to adapt to a changing climate, we must address basic research questions about the changing characteristics of winter snowpack and resulting streamflow that we have long relied upon.”

The research, in collaboration with Zoo Atlanta, finds that an elephant’s skin doesn’t uniformly stretch. The top of the trunk is more flexible than the bottom, and the two sections begin to diverge when an elephant reaches more than 10%. When stretching for food or objects, the dorsal section of the trunk slides further forward.

The findings could improve robotics, which today are typically built for either great strength or flexibility. Unlike an elephant’s trunk, the machines can’t do both.

As an example, the study’s authors point to soft robotics. Their fluid-filled cavities allow flexible movements but can easily break when forces are applied. The researchers say the elephant findings suggest that wrapping soft robotics with a skin-like structure could give the machines protection and strength while continuing to allow flexibility.

The paper is published in the Proceedings of the National Academy of Sciences (PNAS) by the same Georgia Tech team that authored a study last summer about how elephants use their trunk muscles to inhale food and water.

“When people extend their tongue — a muscle-filled, boneless tissue similar in composition to an elephant’s trunk — it stretches uniformly. We expected the same when we challenged an elephant to reach for food,” said Andrew Schulz, the study’s lead author and a Ph.D. student in Georgia Tech’s George W. Woodruff School of Mechanical Engineering. He and the team filmed two African savanna elephants reaching for bran cubes and apples at Zoo Atlanta.

“But when we looked at our high-speed camera footage and plotted the trunk’s movements, we were surprised. The top and bottom weren’t the same at all,” Schulz said.

After seeing the video, Schulz stretched the tissue of a dissected elephant to better understand the skin’s elasticity. That’s when he found that the top of the skin, which is folded, is 15% more flexible than the wrinkled bottom side. It’s also when the team realized they weren’t just seeing muscle movement on the video. They were also tracking a thick sheet of skin.

“Flexible skin folds are the elephant’s innovation,” said David Hu, Schulz’s advisor and a professor in the Woodruff School and the School of Biological Sciences. “They protect the dorsal section and make it easier for the elephant to reach downward, the most common gripping style when picking up items.”

The Georgia Tech study also found that an elephant trunk differs in another way from other boneless, muscle-filled appendages found in nature, such as squid and octopus tentacles. Instead of extending evenly, an elephant telescopically stretches its trunk like an umbrella, gradually lengthening in waves.

An elephant first extends the section that includes the tip of its trunk, then the adjacent section and so on, gradually working its way back toward its body. Schulz says the progressive movement towards the base is intentional.

“Elephants are like people: they’re lazy,” he said. “The section at the end of the trunk is 1 liter of muscle. The section closest to its mouth is 11-15 liters of muscle. An elephant will first stretch the end of its trunk, then the adjacent section, because they’re easier to move. If an elephant doesn’t have to work very hard to reach something, it won’t.”

Schulz said he had to rely on a drawing from 1908 when learning about trunk anatomy because scientists and engineers haven’t done much research on the biomechanics of elephants during the last century. Part of his curiosity of elephants is based on helping them; he thinks a better understanding of the animals will lead to better conservation efforts. As a mechanical engineer, Schulz also sees the applications of robotics.

“Soft robotics created with biologically inspired design are always based on muscle movement. If they were wrapped with a protective skin, like an elephant’s muscle-filled trunk, the machines could apply larger forces,” he said. “Last year we learned that a trunk is a multi-purpose, muscular hydrostat. Now we know that skin is another tool at its disposal.”

“Being able to read and respond to someone’s body language is important in our daily interactions with others,” said Emily Knight, M.D., Ph.D., clinical and postdoctoral fellow in Pediatrics and Neuroscience, is the first author of the study recently published in Molecular Autism. “Our findings suggest that when children with autism are distracted by something else, their brains process the movements of another person differently than their peers.”

Key differences in brain processes

Using electroencephalogram (EEG), researchers recorded the brain waves of children with and without autism as they watched videos of moving dots that were arranged to look like a person. In these videos the dots moved to represent actions such as running, kicking, or jumping, and at times were turned in different directions or jumbled to no longer move like a person. The six- to 16-year-olds were asked to either focus on the color of the dots or to focus on whether the dots moved like a person. Researchers found the brainwaves of children with autism did not process when the dots moved like a person if they were focused on the dot color.

“If their brain is processing body movements less they might have a harder time understanding other people, and need to pay extra attention to body language in order to see it,” said Knight. “Knowing this can help guide new ways to support people with autism.”

“This is more evidence of how the brain of someone with autism is processing the world around them,” said John Foxe, Ph.D., lead author of the study. “This research is a vital step in creating a more inclusive space for people with autism by giving a glimpse of how their brain processes an unspoken part of communication.”

Additional authors include Ed Freedman, Ph.D., from the University of Rochester Medical Center, John Butler, Ph.D., Aaron Krakowski, and Sophie Molholm, Ph.D., of Einstein College of Medicine. This research was supported by the National Institute of Mental Health and the University of Rochester Intellectual and Developmental Disability Research Center (UR-IDDRC) and the Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (RFK-IDDRC).

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Materials provided by University of Rochester Medical Center. Original written by Kelsie Smith Hayduk. Note: Content may be edited for style and length.