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Open Access Highly Accessed Methodology article

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue

Walter K Schlage1, Jurjen W Westra2, Stephan Gebel1, Natalie L Catlett2, Carole Mathis3, Brian P Frushour2, Arnd Hengstermann1, Aaron Van Hooser2, Carine Poussin3, Ben Wong2, Michael Lietz3, Jennifer Park2, David Drubin2, Emilija Veljkovic3, Manuel C Peitsch3, Julia Hoeng3* and Renee Deehan2

Author affiliations

1 Philip Morris International R&D, Philip Morris Research Laboratories GmbH, Fuggerstr.3, 51149 Koeln, Germany

2 Selventa, One Alewife Center, Cambridge, MA 02140, USA

3 Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland

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Citation and License

BMC Systems Biology 2011, 5:168  doi:10.1186/1752-0509-5-168

Published: 19 October 2011

Abstract

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.