By characterizing physiological changes that occur in warfighters during simulated combat, we can start to unravel the key biomolecular components that are linked to physical and cognitive performance. Viable field-based sensors for the warfighter must be rapid and noninvasive. In an effort to facilitate this, we applied a multiomics pipeline to characterize the stress response in the saliva of warfighters to correlate biomolecular changes with overall performance and health. In this study, two different stress models were observed – one of chronic stress and one of acute stress. In both models, significant perturbations in the immune, metabolic, and protein manufacturing/processing systems were observed. However, when differentiating between stress models, specific metabolites associated with the “fight or flight” response and protein folding were seen to be discriminate of the acute stress model.

Sulfur mustard (HD) poses a serious threat due to its relatively simple production process. Exposure to HD in the short-term causes an inflammatory response, while long-term exposure results in DNA and RNA damage. Respiratory tract tissue models were exposed to relatively low concentrations of HD and collected at 3 and 24 h post exposure. Histology, cytokine ELISAs, and mass spectrometric-based analyses were performed. Histology and ELISA data confirmed previously seen lung damage and inflammatory markers from HD exposure. The multi-omic mass spectrometry data showed variation in proteins and metabolites associated with increased inflammation, as well as DNA and RNA damage. HD exposure causes DNA and RNA damage that results in variation of proteins and metabolites that are associated with transcription, translation and cellular energy.

Despite the recent epidemic of fentanyl abuse, there are few validated assays capable of rapidly detecting these compounds. In order to improve the ability to detect carfentanil at physiologically relevant concentrations, we developed a systems biology approach to discover host-based markers which are specifically amplified upon exposure in a rabbit model. For this work, two omics pipelines utilizing mass spectrometry were developed and leveraged. First, a proteomics pipeline was developed to interrogate the blood plasma for protein-based biomarkers. Due to the incredible dynamic range of the plasma protein content, a multi-dimensional fractionation technique was used to partition and more accurately investigate the circulating plasma proteome. Isobaric tandem mass tags were integrated into the workflow to make quantitative assessments across all animals for an extended time course post-exposure. In addition to the proteomics efforts, blood plasma was also processed through an untargeted metabolomics pipeline. This approach allows for the identification of >800 small molecule features. By processing and analyzing data sets in parallel, we were able to identify a unique fingerprint of protein and metabolite perturbations that manifest following exposure to carfentanil.