Environmental Factor – April 2022: Computer Modeling Reveals Toxic Chemicals in Wildfire Smoke

During a March 8 webinar sponsored by the NIEHS Mixtures Cross-Divisional Group, toxicologist Julia Rager, Ph.D.from the University of North Carolina at Chapel Hill (UNC), spoke about his efforts to model and sample toxic exposures to smoke from wildfires.

Wildfires can result in the inhalation of complex mixtures of chemicals, adversely affecting the health of firefighters and nearby residents. (Photo courtesy of David A. Litman/Shutterstock.com)

“If you’re attending this webinar, I’m sure you know that complex environmental exposures from a variety of sources can harm our health,” said Mark Miller, Ph.D., of the institute’s division of the National Program of toxicology. (DNTP), which featured Rager.

“Developing a better understanding of exposome, mixtures and combined exposures has been a major goal of NIEHS for many years,” he added. “While we’ve made significant progress, there’s still a lot we don’t know, and that’s why we’re all here today.”

Wildfires create complex exposures

Julia Rager, Ph.D. “Wildfires are increasing in both prevalence and intensity,” Rager said. “Around the world, every year, they contribute to poor air quality.” (Photo courtesy of Julia Rager)

“One of the most interesting mixtures to study is inhalation exposure to wildfires,” Rager said. “It’s so variable and complex, making it a perfect choice for bioinformatics analyses.”

She combines computer modeling with cell and animal testing to study the complex exposure conditions of forest fires. Rager aims to accomplish the following.

  • Identify the chemical drivers of wildfire-induced toxicity.
  • Identify exposure conditions relevant to wildfires that are similar enough to merit study or regulation as a group.
  • Describe the biological mechanisms underlying the health effects associated with wildfires, including the role of toxicological messengers called extracellular vesicles (EVs).

Rager also described the training materials she developed at the NIEHS-supported UNC Superfund Research Program. For example, his lab has created a publicly available data science training toolkit called intelligence and machine learning (TAME). This resource includes a series of educational modules that help students apply computational approaches to researching environmental exposures.

Understanding Toxicity

“Wildfire exposure assessments can be difficult because our studies are typically retroactive in nature,” Rager said. “With simulations, it is difficult to estimate the chemistry and particle concentrations that occur in nature.”

She referenced research by scientists at the US Environmental Protection Agency (EPA) that influenced her work. They used specialized equipment to collect smoke samples of various types of biofuels common to wildfires around the world – including eucalyptus, peat, pine, pine needles and red oak – during different phases. combustion, such as smoldering versus burning.

Rager teamed up with EPA researchers, using banked tissue samples from exposed mice to determine which chemicals in the complex smoke mixtures produced by various biomass combustion scenarios lead to cardiopulmonary toxicity.

“We incorporated a suite of computational mixture approaches to identify groups of chemicals associated with different biological responses,” she said.

The researchers looked at biomarkers, immune cell counts, markers of lung damage and other data. Rager also looked for genetic markers — similar gene expressions in different samples — in response to wildfire exposure.

Additionally, his lab used a method called Sufficient Similarity Analysis, in which groups of exposure conditions are evaluated for their chemical and biological similarity.

“We identified concurrent chemical cluster modules in different mass burning scenarios,” she said. “We believe these modules contain the worst actors present in wildfire exhibits.”

Cardiovascular outcomes

In trying to understand the body’s reaction to smoke inhalation, Rager recently looked at a key element of molecular communication called extracellular vesicles (VE).

“These are particles that are released by the mother cells, [and] they contain and transport molecules from parent cells to potential target cells,” she said. “We posit that wildfire exposures cause hypoxia and cellular stress in the lungs, leading to the release of EVs that impact heart and cardiovascular outcomes.”

Inform future research

“It’s impossible to test all of the chemicals that occur in wildfires,” Rager told attendees. ” Benefit [computer-based] Mixture modeling techniques are very useful in reducing reliance on animal testing and informing future research,” she said.

“And by identifying key toxicity factors within different biomass sources, we can improve risk characterization in complex exposures,” Rager added. “This will help identify geographic regions likely to be most exposed to smoke from wildfires.”

Rager JE, Clark J, Eaves LA, Avula V, Niehoff NM, Kim YH, Jaspers I, Gilmour MI. 2021. Mixture modeling identifies chemical inducers versus repressors of wildfire smoke-associated toxicity. Sci Total About 25(775):145759.

Carberry CK, Keshava D, Payton A, Smith GJ, Rager JE. 2022. Approaches to incorporating extracellular vesicles into exposure science, toxicology, and public health research. J Expo Sci Environ Epidemiol; doi:10.1038/s41370-022-00417-w [Online 25 February 2022].

(John Yewell is contract writer for the NIEHS Office of Communications and Public Liaison.)