Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring
Introduction:
The prevalence and intensities of wildfires continue to grow world-wide, with exposures resulting in increased risk of pulmonary and cardiovascular disease. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from variable burn conditions and biomass fuel types.
Objectives:
This study tested the hypothesis that select biomass burn scenarios group together based on similar transcriptional response profiles, informing which wildfire-relevant exposure scenarios are the most toxic, and which exposures may be considered as a group for health risk evaluations.
Methods:
Mice (female CD-1) were exposed to biomass burn scenarios produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Transcriptomic signatures were evaluated in lung tissues collected 4 and 24h post-exposure and used to calculate transcriptomic similarity scores between each exposure pair. These similarity scores informed exposure groupings that were anchored to cardiopulmonary toxicity endpoints, including markers of tissue inflammation and injury, and compared to the pro-inflammatory agent, lipopolysaccharide (LPS). Groupings based on exposure chemistries were also compared.
Results:
Biomass burns elicited differential response patterns across the transcriptome with transcriptomic similarity scores reflective of the derived exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest transcriptomic responses, with flaming peat grouping together with LPS. Smoldering red oak and smoldering peat induced the least amount of transcriptomic responses. Transcriptional changes showed enrichment for pathways involved in cell stress, hypoxia, and inflammation. Groupings paralleled trends in cardiopulmonary toxicity markers, though were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Groupings based on exposure chemistry signatures were largely different than transcriptomic and toxicity-based groupings.
Conclusions:
Transcriptomic similarity scoring successfully informed exposure condition groupings according to biological responses, yielding insight into human health risk assessment strategies to ultimately protect public health.