Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

• Published in: The Science of the total environment Vol. 775; p. 145759
• Main Authors: Rager, Julia E., Clark, Jeliyah, Eaves, Lauren A., Avula, Vennela, Niehoff, Nicole M., Kim, Yong Ho, Jaspers, Ilona, Gilmour, M. Ian
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 25-06-2021
• Subjects: Air pollution; Animals; Biomass burns; Cluster Analysis; Complex mixtures; Computational toxicology; Mice; Mixtures toxicology; Pulmonary effects; Smoke - adverse effects; Wildfires; Mn; WGS; WCCNA; COX-2; MIP-2; Biomass burns; Pulmonary effects; WGCNA; LPS; Cr; P; LDH; NF-κB; Mixtures toxicology; Cu; Na; Zn; FDR; Ti; Complex mixtures; Computational toxicology; HSF1; SO4; Ni; Fe; LOD; BALF; IL-6; Sb; TNF-α; GGT; NAG; PAHs; Air pollution; Mg; PM; Ca; Sr
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2021.145759

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.
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•Mice exposed to biomass burn samples representing different wildfire scenarios•Biological responses were evaluated including pulmonary immune and injury markers.•Clustering, data reduction, and quantile g-computational methods addressed mixtures.•Methoxyphenols suggested to reduce responses in presence of other chemicals•Inorganics and ionic constituents suggested to induce biological responses