impact-assessment-repeated-exposure-organotypic-3d-bronchial-nasal

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Cigarette smoke (CS) has a major impact on lung biology and may result in the development of lung diseases such as chronic obstructive pulmonary disease ...

Philip Morris Products S.A.

Nicotine, a large constituent of cigarette smoke, is associated with an increased risk of lung cancer, but the data supporting this relationship are inconsistent. Here, we found that nicotine treatment not only induced emphysema but also increased both lung tumor multiplicity and volume in 4-nitrosamino-1-(3-pyridyl)-1-butanone (NNK)-initiated lung cancer in A/J mice. This tumor-promoting effect of nicotine was accompanied by significant reductions in survival probability and lung Sirtuin 1 (SIRT1) expression, which has been proposed as a tumor suppressor. The decreased level of SIRT1 was associated with increased levels of AKT phosphorylation and interleukin (il)-6 mRNA but decreased tumor suppressor p53 and retinoic acid receptor (RAR)-β mRNA levels in the lungs. Using this mouse model, we then determined whether β-cryptoxanthin (BCX), a xanthophyll that is strongly associated with a reduced risk of lung cancer in several cohort studies, can inhibit nicotine-induced emphysema and lung tumorigenesis. We found that BCX supplementation at two different doses was associated with reductions of the nicotine-promoted lung tumor multiplicity and volume, as well as emphysema in mice treated with both NNK and nicotine. Moreover, BCX supplementation restored the nicotine-suppressed expression of lung SIRT1, p53, and RAR-β to that of the control group, increased survival probability, and decreased the levels of lung il-6 mRNA and phosphorylation of AKT. The present study indicates that BCX is a preventive agent against emphysema and lung cancer with SIRT1 as a potential target. In addition, our study establishes a relevant animal lung cancer model for studying tumor growth within emphysematous microenvironments.

β-cryptoxanthin restores nicotine-reduced lung SIRT1 to normal levels and inhibits nicotine-promoted lung tumorigenesis and emphysema in A/J mice.

Twenty first century systems toxicology approaches enable the discovery of biological pathways affected in response to active substances. Here, we briefly summarize current network approaches that facilitate the detailed mechanistic understanding of the impact of a given stimulus on a biological system. We also introduce our network-based method with two use cases and show how causal biological network models combined with computational methods provide quantitative mechanistic insights. Our approach provides a robust comparison of the transcriptional responses in different experimental systems and enables the identification of network-based biomarkers modulated in response to exposure. These advances can also be applied to pharmacology, where the understanding of disease mechanisms and adverse drug effects is imperative for the development of efficient and safe treatment options.

Case study: the role of mechanistic network models in systems toxicology.

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

On Crowd-verification of Biological Networks.

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.

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

Smoking has been associated with diseases of the lung, pulmonary airways and oral cavity. Cytologic, genomic and transcriptomic changes in oral mucosa correlate with oral pre-neoplasia, cancer and inflammation (e.g. periodontitis). Alteration of smoking-related gene expression changes in oral epithelial cells is similar to that in bronchial and nasal epithelial cells. Using a systems toxicology approach, we have previously assessed the impact of cigarette smoke (CS) seen as perturbations of biological processes in human nasal and bronchial organotypic epithelial culture models. Here, we report our further assessment using in vitro human oral organotypic epithelium models. We exposed the buccal and gingival organotypic epithelial tissue cultures to CS at the air-liquid interface. CS exposure was associated with increased secretion of inflammatory mediators, induction of cytochrome P450s activity and overall weak toxicity in both tissues. Using microarray technology, gene-set analysis and a novel computational modeling approach leveraging causal biological network models, we identified CS impact on xenobiotic metabolism-related pathways accompanied by a more subtle alteration in inflammatory processes. Gene-set analysis further indicated that the CS-induced pathways in the in vitro buccal tissue models resembled those in the in vivo buccal biopsies of smokers from a published dataset. These findings support the translatability of systems responses from in vitro to in vivo and demonstrate the applicability of oral organotypical tissue models for an impact assessment of CS on various tissues exposed during smoking, as well as for impact assessment of reduced-risk products.

In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures.

Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

Article	Year
β-cryptoxanthin restores nicotine-reduced lung SIRT1 to normal levels and inhibits nicotine-promoted lung tumorigenesis and emphysema in A/J mice. Cancer prevention research (Philadelphia, Pa.)\| PubMed ID: 23275008	2013
Case study: the role of mechanistic network models in systems toxicology. Drug discovery today\| PubMed ID: 23933191	2014
On Crowd-verification of Biological Networks. Bioinformatics and biology insights\| PubMed ID: 24151423	2013
Systems approaches evaluating the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues. BioMed research international\| PubMed ID: 24224167	2013
In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures. Toxicology mechanisms and methods\| PubMed ID: 25046638	2014
A crowd-sourcing approach for the construction of species-specific cell signaling networks. Bioinformatics (Oxford, England)\| PubMed ID: 25294919	2014

Anita Iskandar

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