Publications

INTRODUCTION

Ventilator-induced lung injury (VILI) impacts clinical outcomes in acute respiratory distress syndrome (ARDS), which is characterized by neutrophil-mediated inflammation and loss of alveolar barrier function. Recent epidemiological studies suggest that smoking may be a risk factor for the development of ARDS. Because alveolar type II cells are central to maintaining the alveolar epithelial barrier during oxidative stress, mediated in part by neutrophilic inflammation and mechanical ventilation, we hypothesized that exposure to cigarette smoke and mechanical strain have interactive effects leading to the activation of and damage to alveolar type II cells.

METHODS

To determine if cigarette smoke increases susceptibility to VILI in vivo, a clinically relevant rat model was established. Rats were exposed to three research cigarettes per day for two weeks. After this period, some rats were mechanically ventilated for 4 hours. Bronchoalveolar lavage (BAL) and differential cell count was done and alveolar type II cells were isolated. Proteomic analysis was performed on the isolated alveolar type II cells to discover alterations in cellular pathways at the protein level that might contribute to injury. Effects on levels of proteins in pathways associated with innate immunity, oxidative stress and apoptosis were evaluated in alveolar type II cell lysates by enzyme-linked immunosorbent assay. Statistical comparisons were performed by t-tests, and the results were corrected for multiple comparisons using the false discovery rate.

RESULTS

Tobacco smoke exposure increased airspace neutrophil influx in response to mechanical ventilation. The combined exposure to cigarette smoke and mechanical ventilation significantly increased BAL neutrophil count and protein content. Neutrophils were significantly higher after smoke exposure and ventilation than after ventilation alone. DNA fragments were significantly elevated in alveolar type II cells. Smoke exposure did not significantly alter other protein-level markers of cell activation, including Toll-like receptor 4; caspases 3, 8 and 9; and heat shock protein 70.

CONCLUSIONS

Cigarette smoke exposure may impact ventilator-associated alveolar epithelial injury by augmenting neutrophil influx. We found that cigarette smoke had less effect on other pathways previously associated with VILI, including innate immunity, oxidative stress and apoptosis.

As top-level predators, common bottlenose dolphins (Tursiops truncatus) are particularly sensitive to chemical and biological contaminants that accumulate and biomagnify in the marine food chain. This work investigates the potential use of microarray technology and gene expression profile analysis to screen common bottlenose dolphins for exposure to environmental contaminants through the immunological and/or endocrine perturbations associated with these agents. A dolphin microarray representing 24,418 unigene sequences was used to analyze blood samples collected from 47 dolphins during capture-release health assessments from five different US coastal locations (Beaufort, NC, Sarasota Bay, FL, Saint Joseph Bay, FL, Sapelo Island, GA and Brunswick, GA). Organohalogen contaminants including pesticides, polychlorinated biphenyl congeners (PCBs) and polybrominated diphenyl ether congeners were determined in blubber biopsy samples from the same animals. A subset of samples (n = 10, males; n = 8, females) with the highest and the lowest measured values of PCBs in their blubber was used as strata to determine the differential gene expression of the exposure extremes through machine learning classification algorithms. A set of genes associated primarily with nuclear and DNA stability, cell division and apoptosis regulation, intra- and extra-cellular traffic, and immune response activation was selected by the algorithm for identifying the two exposure extremes. In order to test the hypothesis that these gene expression patterns reflect PCB exposure, we next investigated the blood transcriptomes of the remaining dolphin samples using machine-learning approaches, including K-nn and Support Vector Machines classifiers. Using the derived gene sets, the algorithms worked very well (100% success rate) at classifying dolphins according to the contaminant load accumulated in their blubber. These results suggest that gene expression profile analysis may provide a valuable means to screen for indicators of chemical exposure.