Diisocyanate (DI) exposure is a prominent cause of occupational asthma and an estimated 280,000 workers are exposed to isocyanates in the US alone. Workers who use products containing DI are exposed to both monomeric and polymeric DIs and ?10% develop DI-induced asthma (DA). DA has been linked to skin and inhalation exposure to DIs in humans, guinea pigs, and mice. The sensitization capacity of polymeric DIs has been shown to be greater than monomers in both humans and animals. Further, polymeric DI exposure encompasses the vast majority of the total exposure in DI-exposed workers. DA is likely caused by inter-individual differences in levels of exposure and inherited genetic variants. Therefore, it is critical to determine the relative potency and dose-relationships between monomeric and polymeric DIs on the development of DA. Our goal is to better understand the genetic and mechanistic basis for DA susceptibility. Due to the strong sensitization capacity of DIs and their potential presence in consumer products, sensitization studies using human volunteers is unethical. Thus, we propose to use the genetically diverse Collaborative Cross recombinant inbred lines (CC RIL), an experimental mouse model, to investigate DA susceptibility. To accomplish our goal, we will identify inter-individual (strain) differences and the dose-response relationships between 1,6-hexamethylene diisocyanate (HDI) monomer and its oligomer HDI isocyanurate, model chemicals that have been shown to cause DA, on the development of allergic airway inflammation response in homozygous inbred BALB/cJ and CC RIL mice. Preclinical animal toxicity studies of DI allergenicity have been performed mostly in homozygous inbred BALB/cJ mice. Genetic diversity in a single or limited number of inbred mice, like BALB/cJ, may limit translation of findings to human DA susceptibility. CC RIL mice have a genetic diversity (?45 million genome-wide genetic markers) and genomic architecture similar to human populations but with a balanced minor allele frequency of 12%, which greatly increases power for identification of genetic variants using genome-wide association studies. By defining inter-individual differences and a mechanistic basis for a benchmark response to occupational exposure stressors, we can guide exposure and risk assessment in human populations. The scope of this research is consistent and amplifies NIOSH goals benefitting two NIOSH NORA sectors (Services and Manufacturing), the new NORA Immune, Infectious and Dermal Disease Prevention Cross-Sector goals, and NIOSH r2p initiative to reduce and prevent occupational diseases. The Outputs and Outcomes from this study can be summarized as follows: this experimental model will be a significant step forward in the (1) development of predictive in vivo models to discover causal variants, (2) identification of biomarkers of exposure relevant to humans, (3) determination of a benchmark-dose response, (4) determination of the systems-level adverse outcome pathways for DI skin-airway sensitization and elicitation of delayed-type immunity in the airways, (5) estimating variance for designing future gene- environment interaction mapping studies to identify genetic loci associated with either resistance or susceptibility to airway inflammation following skin sensitization, and (6) establishing a genetic basis for DA benchmark-dose models for risk assessment.
Inhalation and skin exposure to diisocyanates is a prominent cause of occupational asthma. We will test the difference in skin-airway sensitization potential between two commonly used diisocyanates to determine a benchmark-dose response and to demonstrate inter-individual differences in their relative sensitization potential using a new genetically-diverse mouse model that will allow investigation of exposure-outcome relationships while controlling potential confounding variables. The results obtained in this project will increase our knowledge and data available for investigation of gene- environment interactions in occupational and epidemiology studies and lead to more predictive exposure assessment models that, in turn, will allow us to develop better worker protection strategies.
