Lung proteomic biomarkers associated with chronic obstructive pulmonary disease.

Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have used individual proteins or preselected protein panels measured in blood samples. Mass spectrometry proteomic studies of lung tissue have been based on small sample sizes. We used mass spectrometry proteomic approaches to discover protein biomarkers from 150 lung tissue samples representing COPD cases and controls. Top COPD-associated proteins were identified based on multiple linear regression analysis with false discovery rate (FDR) < 0.05. Correlations between pairs of COPD-associated proteins were examined. Machine learning models were also evaluated to identify potential combinations of protein biomarkers related to COPD. We identified 4,407 proteins passing quality controls. Twenty-five proteins were significantly associated with COPD at FDR < 0.05, including interleukin 33, ferritin (light chain and heavy chain), and two proteins related to caveolae (CAV1 and CAVIN1). Multiple previously reported plasma protein biomarkers for COPD were not significantly associated with proteomic analysis of COPD in lung tissue, although RAGE was borderline significant. Eleven pairs of top significant proteins were highly correlated (r > 0.8), including several strongly correlated with RAGE (EHD2 and CAVIN1). Machine learning models using Random Forests with the top 5% of protein biomarkers demonstrated reasonable accuracy (0.707) and area under the curve (0.714) for COPD prediction. Mass spectrometry-based proteomic analysis of lung tissue is a promising approach for the identification of biomarkers for COPD.

Characterization of atmospheric ammonia emissions from a commercial chicken house on the Delmarva Peninsula.

A three-dimensional sampling grid using passive collectors was used to characterize the downwind gas-phase ammonia plumes originating from a commercial chicken house on the Delmarva Peninsula in the Chesapeake Bay watershed. Inverse Gaussian plume modeling was used to determine the source strength of the chicken house and the corresponding chicken emission factors. A total of seven field deployments were performed during two different flocks with a sampling duration ranging from 6 to 12.6 h. The deployments occurred during weeks 3, 4, and 5 of the 6-week chicken grow-out period in the months of May-July 2002. The ammonia emission factors ranged from 0.27 to 2.17 g of NH3-N bird(-1) day(-1) with a mean of 1.18 g of NH3-N bird(-1) day(-1). Weighted emissions factors that accounted for the nonlinear increase in ammonia emissions over the 6-week grow-out period were also calculated and ranged from 0.14 to 1.65 g of NH3-N bird(-1) day(-1) with a mean of 0.74 g of NH3-N bird(-1) day(-1). These weighted emission values would correspond to an annual release of approximately 18 x 10(6) kg of NH3-N to the atmosphere from broiler production on the Delmarva Peninsula. This assumes that the emission factors in this study are representative for the entire year with varying meteorological conditions and are representative of all chicken husbandry practices. The Delmarva Peninsula could represent a significant source of nutrient nitrogen to the Chesapeake Bay and Delaware Bay watersheds through atmospheric deposition when considering the size of this annual release rate, the relative short atmospheric lifetime of ammonia due to deposition, and the proximity of the Delmarva Peninsula to the Chesapeake and Delaware Bays.