Systems approaches evaluating the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues

Abstract

Capturing the effects of exposure in a specific target organ is a major challenge in risk assessment. Exposure to cigarette smoke (CS) implicates the field of tissue injury in the lung as well as nasal and airway epithelia. Xenobiotic metabolism in particular becomes an attractive tool for chemical risk assessment because of its responsiveness against toxic compounds, including those present in CS. This study describes an efficient integration from transcriptomic data to quantitative measures, which reflect the responses against xenobiotics that are captured in a biological network model. We show here that our novel systems approach can quantify the perturbation in the network model of xenobiotic metabolism. We further show that this approach efficiently compares the perturbation upon CS exposure in bronchial and nasal epithelial cells in vivo samples obtained from smokers. Our observation suggests the xenobiotic responses in the bronchial and nasal epithelial cells of smokers were similar to those observed in their respective organotypic models exposed to CS. Furthermore, the results suggest that nasal tissue is a reliable surrogate to measure xenobiotic responses in bronchial tissue.

Figure 1

A network model representing the mechanism of xenobiotic metabolism and an illustration of network perturbation amplitude (NPA) approach.

Figure 2

Organotypic bronchial (a) and nasal (b) models. The in vitro models contained ciliated cells shown in the apical layer of the Hematoxylin and Eosin stained cells (left). The models were cocultured with fibroblasts that are important for the growth and differentiation of epithelial cells (indicated by arrows). Staining of airway lineage markers: p63 and Muc5AC are shown (center and right).

Figure 3

Comparability of GSE16008 dataset to other publicly available datasets. Correlations among the differential network backbone values from different human datasets in the xenobiotic metabolism network were shown. The human datasets comprise smoker versus nonsmoker data. Each data point represents a backbone node in the network. The 95%-confidence intervals of the differential network backbone values are shown for the two perturbations (axes). Blue lines show the linear regression lines computed by least squares fit. All the regression models were significant (P value < 0.05). Insets illustrate the correlation of the fold change of gene expressions.

Figure 4

Correlation between the differential network backbone values in response to CS exposure generated from in vivo human bronchial and nasal datasets in the xenobiotic metabolism network model.

Figure 5

Comparison between xenobiotic metabolism responses in organotypic bronchial and nasal epithelia in vitro models upon CS exposure.

Figure 6

Correlations between the differential network backbone values in response to CS exposure generated from in vivo datasets and in vitro organotypic models in the xenobiotic metabolism network model.

Figure 7

Our present work could be implemented to the new generation of “omics” technology for the overall assessment of CS exposure pertaining to the perturbation of xenobiotic metabolism. This current work provided an useful example for the utilization of transcriptomic data for impact assessment that focuses on xenobiotic responses against airborne exposure.