Organotypic culture of human primary bronchial epithelial cells is a useful in vitro system to study normal biological processes and lung disease mechanisms, to develop new therapies, and to assess the biological perturbations induced by environmental pollutants. Herein, we investigate whether the perturbations induced by cigarette smoke (CS) and observed in the epithelium of smokers' airways are reproducible in this in vitro system (AIR-100 tissue), which has been shown to recapitulate most of the characteristics of the human bronchial epithelium. Human AIR-100 tissues were exposed to mainstream CS for 7, 14, 21, or 28 min at the air-liquid interface, and we investigated various biological endpoints [e.g., gene expression and microRNA profiles, matrix metalloproteinase 1 (MMP-1) release] at multiple postexposure time points (0.5, 2, 4, 24, 48 h). By performing a Gene Set Enrichment Analysis, we observed a significant enrichment of human smokers' bronchial epithelium gene signatures derived from different public transcriptomics datasets in CS-exposed AIR-100 tissue. Comparison of in vitro microRNA profiles with microRNA data from healthy smokers highlighted various highly translatable microRNAs associated with inflammation or with cell cycle processes that are known to be perturbed by CS in lung tissue. We also found a dose-dependent increase of MMP-1 release by AIR-100 tissue 48 h after CS exposure in agreement with the known effect of CS on this collagenase expression in smokers' tissues. In conclusion, a similar biological perturbation than the one observed in vivo in smokers' airway epithelium could be induced after a single CS exposure of a human organotypic bronchial epithelium-like tissue culture.

Background: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.Results: Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.Conclusions: The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Background: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate. Results: We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung. Conclusions: The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.

This study reports a comparative assessment of the biological impact of a heated tobacco aerosol from the tobacco heating system (THS) 2.2 and smoke from a combustible 3R4F cigarette. Human organotypic bronchial epithelial cultures were exposed to an aerosol from THS2.2 (a candidate modified-risk tobacco product) or 3R4F smoke at similar nicotine concentrations. A systems toxicology approach was applied to enable a comprehensive exposure impact assessment. Culture histology, cytotoxicity, secreted pro-inflammatory mediators, ciliary beating, and genome-wide mRNA/miRNA profiles were assessed at various time points post-exposure. Series of experimental repetitions were conducted to increase the robustness of the assessment. At similar nicotine concentrations, THS2.2 aerosol elicited lower cytotoxicity compared with 3R4F smoke. No morphological change was observed following exposure to THS2.2 aerosol, even at nicotine concentration three times that of 3R4F smoke. Lower levels of secreted mediators and fewer miRNA alterations were observed following exposure to THS2.2 aerosol than following 3R4F smoke. Based on the computational analysis of the gene expression changes, 3R4F (0.13 mg nicotine/L) elicited the highest biological impact (100%) in the context of Cell Fate, Cell Proliferation, Cell Stress, and Inflammatory Network Models at 4 h post-exposure. Whereas, the corresponding impact of THS2.2 (0.14 mg nicotine/L) was 7.6%.

Background: Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work. Results: To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data. Conclusions: To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.

Cigarette smoking causes serious and fatal diseases. The best way for smokers to avoid health risks is to quit smoking. Using modified risk tobacco products (MRTPs) may be an alternative to reduce the harm caused for those who are unwilling to quit smoking, but little is known about the toxic effects of MRTPs, nor were the molecular mechanisms of toxicity investigated in detail. The toxicity of an MRTP and the potential molecular mechanisms involved were investigated in high-content screening tests and whole genome transcriptomics analyses using human bronchial epithelial cells. The prototypic (p)MRTP that was tested had less impact than reference cigarette 3R4F on the cellular oxidative stress response and cell death pathways. Higher pMRTP aerosol extract concentrations had impact on pathways associated with the detoxification of xenobiotics and the reduction of oxidative damage. A pMRTP aerosol concentration up to 18 times higher than the 3R4F caused similar perturbation effects in biological networks and led to the perturbation of networks related to cell stress, and proliferation biology. These results may further facilitate the development of a systems toxicology-based impact assessment for use in future risk assessments in line with the 21st century toxicology paradigm, as shown here for an MRTP.