In a new study published this week in the Journal of Biogeography, researchers from the University of California, Berkeley, provide the first detailed description of the stunning array of fungi that make their home on the Polynesian island of Mo’orea. The collection includes more than 200 species of macrofungi — that is, fungi producing visible, fruiting bodies — many of which may be new to science.

“It’s like a treasure trove,” said study lead author Matteo Garbelotto, cooperative extension specialist and adjunct professor of environmental science, policy and management at UC Berkeley. “It’s truly uncharted territory in evolutionary biology and biodiversity of the fungal kingdom, and this is one the first attempts to generate baseline information on fungal diversity, not just for Mo’orea, but for the entire and vast Insular Oceania region.”

As part of the Mo’orea Biocode Project, the study team spent months trekking across the island in search of new species of fungi, ultimately gathering a total of 553 fungal specimens and sequencing the DNA of 433 of them. Because only a handful of the sequenced specimens have exact genetic matches with other known species, the Mo’orea collections are likely to contain completely new species.

By comparing the DNA sequences of these fungi to those of other species around the world, the team was also able to piece together where the fungal biodiversity on the remote island may have originated. The findings suggest that the majority of the species, or their ancestors, were carried by easterly winds from Australia or other South Pacific islands, though a small number may have been brought to Mo’orea by humans from far-flung locations like East Asia, Europe and South America.

“We were really interested in the biodiversity of the island,” said study first author Todd Osmundson, who completed the work as a postdoctoral researcher at UC Berkeley. “Mo’orea is an island in the middle of the ocean, and it’s a geologically young volcanic island. It’s never touched another piece of land. How did fungi get there, and where did they come from?”

Understanding both the biodiversity of fungi on the island and how different species have traveled around the world to arrive at this remote location can help as scientists grapple with the ongoing impacts of global travel and trade on biodiversity.

“The Mo’orea BioCode project was the first all-taxa-survey of a tropical island to include DNA vouchers and other associated information. It included all organisms from marine and terrestrial habitats and everything larger than bacteria,” said George Roderick, William Muriece Hoskins Professor of environmental science, policy and management at UC Berkeley. “Since, the data has proven to be enormously valuable in monitoring the impacts of global change on Mo’orea but also on other tropical Pacific Islands.”

‘Every day we had a different challenge’

The Mo’orea Biocode Project was led by Neil Davies, executive director of UC Berkeley’s Gump South Pacific Research Station, and ran from 2007 to 2010. One of the motivations for the project was to create a model ecosystem that could be used to answer fundamental questions about how ecosystems work.

“Fungi are really important parts of ecosystems,” said Osmundson, who is currently a professor of biology at the University of Wisconsin-La Crosse. “They act as primary decomposers, and in some cases (as) pathogens that break down decaying organic matter and recycle the nutrients into forms that other organisms can use. They’re also really important as symbionts. They live with other organisms and benefit that organism in exchange for other things. For instance, some fungi will attach to the roots of plants and exchange nutrients with them.”

To collect the specimens, the research team spent months on Mo’orea, beginning before dawn each day to gather samples of fungi from all corners of the ecosystem, including the soil, the roots and leaves of plants, and even the air.

As the heat and humidity rose throughout the day, the outdoor conditions would often become inhospitable to both the scientists and the delicate fruiting bodies of the fungi they had collected. By early afternoon, they would take their samples back to the lab and begin the process of documenting and culturing the specimens they had found, often staying up late into the night to complete their work.

“The terrain on the island is incredibly steep, and when it rains it becomes incredibly muddy, and a lot of areas are not managed. So, every day we had a different challenge,” Garbelotto said. “There are some slopes that you can only really explore on ropes. I remember being attached to a rope with my hands sticking out on the precipice, trying to collect a mushroom that was growing on a little outcrop where you couldn’t possibly walk.“

Each of the specimens was photographed and dried for storage in the University Herbarium and compared to databases of known species. As part of the biocode project, the research team also obtained DNA sequences of a specific gene that can be used as a unique “barcode” to differentiate one species from another.

“In many ways, Mo’orea is not a pristine island, and that actually makes it more interesting to me,” Garbelotto said. “The island has completely pristine areas and also has areas that have been inhabited and deeply changed by humans, starting with the arrival of Polynesians 3,000 years ago and continuing until relatively recently with the arrival of the French, the English and the Americans. Compared to places that are completely pristine, Mo’orea is more interesting to me because it’s more representative of what the world actually is.”

Additional co-authors of the paper are Sarah E. Bergemann of Middle Tennessee State University and Rikke Rasmussen, who worked on DNA sequencing as a volunteer at UC Berkeley. The Moorea Biocode Project was supported by the Gordon and Betty Moore Foundation.

This research, published in the Proceedings of the National Academy of Sciences, illustrates the importance of a well-known cytokine called interleukin-13 (IL-13) in protecting cells against SARS-CoV-2, which helps explain the mystery of why people with allergic asthma fair better than the general population despite having a chronic lung condition. The same cannot be said for individuals with other diseases, such as chronic obstructive pulmonary disease (COPD) or emphysema, who are at very high risk of severe COVID.

“We knew there had to be a bio-mechanistic reason why people with allergic asthma seemed more protected from severe disease,” said senior author Camille Ehre, PhD, assistant professor of pediatrics at the UNC School of Medicine and member of the UNC Marsico Lung Institute. “Our research team discovered a number of significant cellular changes, particularly due to IL-13, leading us to conclude that IL-13 plays a unique role in defense against SARS-CoV-2 infection in certain patient populations.”

Although cytokines like IL-13 cannot be used as therapies because they trigger inflammation, it is important to understand natural molecular pathways that cells use to protect themselves from pathogen invasion, as these studies have the potential to reveal new therapeutic targets.

There are many health factors that increase a person’s risk of severe COVID, including chronic lung diseases such as COPD, but as the pandemic went on, epidemiologists found that people with allergic asthma were less susceptible to severe disease.

“These are patients with asthma caused by allergens, such as mold, pollen, and dander,” said Ehre, who is also a member of the UNC Children’s Research Institute. “To find out why they are less susceptible, we investigated specific cellular mechanisms in primary human airway epithelial cell cultures.”

The experiments were led by co-first authors Cameron Morrison, a medical student in the Ehre lab, and Caitlin Edwards, a research assistant and MPH student in the lab of Ralph Baric, PhD, Kenan Distinguished Professor of Epidemiology at the UNC Gillings School of Global Public Health and professor in the UNC Department of Microbiology and Immunology at the UNC School of Medicine.

The researchers used genetic analysis of human airway cell cultures infected with SARS-CoV-2 to discover that the expression of the human protein ACE2 governed which cell types were infected and the amount of virus found in this cell population (also known as “viral load.”)

The scientists then used electron microscopy (EM) to identify an intense exodus of virus from infected ciliated cells, which are cells tasked with moving mucus along the airway surface. EM also revealed severe cytopathogenesis — changes inside human cells due to viral infection. And these changes culminating in ciliated cells (packed with virions) shedding away from the airway surface.

“This shedding is what provides a large viral reservoir for spread and transmission of SARS-CoV-2,” Ehre said. “It also seems to increase the potential for infected cells to relocate to deeper lung tissue.”

Further experiments on infected airway cells revealed that a major mucus protein called MUC5AC was depleted inside cells, likely because the proteins were secreted to try to trap invading viruses. But the virus load kept increasing because the cells tasked with producing MUC5AC were overwhelmed in the face of a rampant viral infection.

The researchers knew from epidemiological studies that allergic asthma patients — known to overproduce MUC5AC- were less susceptible to severe COVID. Ehre and colleagues also knew the cytokine IL-13 increased MUC5AC secretion in the lungs when asthma patients faced an allergen.

The scientists decided to mimic asthmatic airways by treating human airway cells with IL-13. They then measured viral titers, viral mRNA, the rate of infected cell shedding, and the overall number of infected cells. Each one was significantly decreased. They found this remained true even when mucus was removed from the cultures, suggesting other factors were involved in the protective effects of IL-13 against SARS-CoV-2.

Bulk RNA-sequencing analyses revealed that IL-13 upregulated genes that control glycoprotein synthesis, ion transport, and antiviral processes — all of which are important in airway immune defense. They also showed that IL-13 reduced the expression of the viral receptor, ACE2, as well as reducing the amount of virus inside cells and cell-to-cell viral transmission.

Taken together, these findings indicate that IL-13 significantly affected viral entry into cells, replication inside cells, and spread of virus, thus limiting the virus’s ability to find its way deeper into the airways to trigger severe disease.

“We think this research further shows how important it is to treat SARS-CoV-2 infection as early as possible,” Ehre said. “And it shows just how important specific mechanisms involving ACE2 and IL-13 are, as we try our best to protect patients from developing severe infections.”

Other authors of the PNAS paper are Kendall Shaffer, Kenza Araba, Jason Wykoff, Danielle Williams, Takanori Asakura, Hong Dang, Lisa Morton, Rodney Gilmore, Wanda O’Neal, and Ric Boucher, all at UNC-Chapel Hill.

This research was funded through grants from the National Institutes of Health and Vertex Pharmaceuticals.

“The DOE calls sounds of specific events the ‘signs of signature,'” said Misra. “In this case, the signature identified the break or mechanical discontinuity of a rock in the earth’s subsurface, especially as the breaks continued to grow or propagate into fractures.”

Misra and his doctoral candidate Rui Liu published their preliminary findings in the May 2022 issue of the Systems and Signals Processing journal.

Why does Basic Energy Sciences want this signature identified? Sounds are often important clues to environmental and security changes. Threatening noises, such as underground explosions, are hard to mistake. But the small sounds of a high-rise building foundation cracking and failing are just as threatening. So, the fundamental sound of rock undergoing mechanical failure is a basic and critical clue worth finding.

“This research goes to the heart of identifying something specific within a massive data set,” said Misra. “An example is credit card transactions. You cannot monitor the whole data set for fraud because the transactions are so varied. You must find some indicative sign, such as a credit card charge in one city to book an airline flight immediately after that same card pays for an Uber in another city. That discrepancy is a signature.”

Previous attempts to pinpoint underground mechanical failures never brought reliable success, but Misra found that an unusual combination of three research methods — supervised machine learning, causal discovery and rapid simulations — could tackle the problem.

The supervised machine learning began with lab experiments in which a multipoint sensor system was placed on the surface of a rock and recorded sound wave-transmission measurements through the material as it cracked and finally failed. Computers monitored the information and were taught which data signatures meant initial, intermediate and end-stage damage. One tell-tale signature that repeatedly traveled up and down across the zero point between positive and negative measurements caught the computer’s attention, once it knew what to look for.

“I can only see the color or shape of something with my eyes,” said Liu. “But machine learning can pick out so many more characteristics from the data. It picked out those positive and negative turnings, and we used that sign to get further results.”

Misra and Liu searched for the causation of each of these turnings to confirm their source. They couldn’t rely on the computer to complete this step because machine learning is not the best interpreter.

“During the heat of the summer, ice cream sales increase and drowning deaths increase,” said Misra. “If you use machine learning or simple statistical methods, they might say people are drowning because people eat ice cream. That’s a correlation. Though they are both related to the summer heat, they are not connected to each other. They each have a different cause. We are looking for causation for these turnings because that’s when they become meaningful.”

Misra and Liu created a workflow that could generate scenarios of various fracture propagations and measured waveforms. Then, they increased the workflow’s speed to rapidly run up to 20,000 different simulations of possibilities for each event. This allowed the researchers to discover the best cause-and-effect explanations.

“We didn’t control how the discontinuity propagated, so there’s a lot of randomness,” said Misra. “Yet, as the fractures grew, despite the differences in direction or length, results showed a similar increase in amplifications or positive and negative turnings across the zero point in the waveforms. So, this is a definite signature of rock failure, which, to the best of my knowledge, was not known prior to this research.”

While the signature discovery is exciting, the project still has several months to go. Misra intends to explore the limits of the data-driven simulations and causal discovery approach. He will also test other methods to see if similar or different results occur.

“What we need to do as scientists, as engineers, is to find causality, find causation,” said Misra. “We tried a lot of different techniques to discover this signature and its causal relationships. A lot of approaches didn’t work, but one did. Now we need to find the limits of what it can do.”

Story Source:

Materials provided by Texas A&M University. Original written by Nancy Luedke. Note: Content may be edited for style and length.

The findings, published today in the peer-reviewed Journal of the American College of Cardiology: Clinical Electrophysiology, have the potential to enhance prevention of sudden cardiac arrest — unexpected loss of heart function — based on key risk factors identified in this study.

“All sudden cardiac arrest is not the same,” explained Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention and lead author of the study. “Until now, no prior research has distinguished between potentially treatable sudden cardiac arrest versus untreatable forms that cause death in almost all instances.”

Out-of-hospital sudden cardiac arrest claims at least 300,000 U.S. lives annually. For those affected, 90% will die within 10 minutes of cardiac arrest.

For this largely fatal condition, prevention would have a profound impact. The biggest challenge, however, lies in distinguishing between those who stand to benefit the most from an implantable cardioverter defibrillator — and those who would not benefit from the electric jolt.

“Defibrillators are expensive and unnecessary for individuals with the type of sudden cardiac arrest that will not respond to an electrical shock,” said Chugh. “However, for patients with treatable, or ‘shockable,’ forms of the disease, a defibrillator is lifesaving.”

Chugh, also a professor and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research, says this new research provides a clinical risk assessment algorithm that can better identify patients at highest risk of treatable sudden cardiac arrest — and thus, a better understanding of those patients who would benefit from a defibrillator.

The risk assessment algorithm consists of 13 clinical, electrocardiogram, and echocardiographic variables that could put a patient at higher risk of treatable sudden cardiac arrest.

The risk factors include diabetes, myocardial infarction, atrial fibrillation, stroke, heart failure, chronic obstructive pulmonary disease, seizure disorders, syncope — a temporary loss of consciousness caused by a fall in blood pressure — and four separate indicators found with an electrocardiogram test, including heart rate.

“This first-of-its-kind algorithm has the potential to improve the way we currently predict sudden cardiac arrest,” said Eduardo Marbán, MD, PhD, executive director of the Smidt Heart Institute and the Mark S. Siegel Family Foundation Distinguished Professor. “If validated in clinical trials, we will be able to better identify high-risk patients and therefore, save lives.”

The research study utilized data from two ongoing multiyear studies founded by Chugh. The Oregon Sudden Unexpected Death Study is a comprehensive assessment of sudden cardiac arrests among the 1 million residents of the Portland, Oregon, metropolitan area.

The Ventura Prediction of Sudden Death in Multiethnic Communities (PRESTO) study is based in Ventura, California, with approximately 850,000 residents. Both studies are unique community partnerships with area residents, as well as first responders, medical examiners and hospital systems that deliver care within the two communities.

Both led by Chugh, the projects — now ongoing in Oregon for nearly 20 years, and more recently in Ventura — provides researchers with unique, community-based information to help determine how best to predict sudden cardiac arrest.

As a next step, Chugh plans to test their risk assessment algorithm, which was funded by the National Heart, Lung, and Blood Institute (R01HL126938 and R01HL145675), in separate prospective studies, as well as randomized clinical trials.