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

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.

Figure 1

Schematic overview of the modular "building block" framework used to construct the Cellular Stress Network. A detailed network model of NRF2 signaling was included in the Oxidative Stress building block. A few examples of relevant transcription factors and kinase cascades included in the network model are shown.

Figure 2

Pie chart summarizing the tissue context origin of causal edges in the Cellular Stress Network (for details, see Additional File 1).

Figure 3

Network model detail. A portion of the network model surrounding NRF2 (NFE2L2) is shown, including transcriptional regulation by KEAP1 and downstream expression targets. Activating direct causal relationships are shown as dark arrows; inhibitory direct causal relationships are shown as edges ending in a knob.

Figure 4

The Cellular Stress Network. Highlighted nodes are Reverse Causal Reasoning (RCR) hypotheses, predicted to have increased or decreased abundance or activity, in the indicated cell stress data sets.

Figure 5

Test data set and mRNA State Change overview. (top) Test data set comparisons. Comparisons of GSE18344 data from 1 day cigarette smoke exposure experiments used to evaluate the Cellular Stress Network model. (bottom) mRNA State Change (SC) overlap between WT and NRF2 KO data sets. WT = wildtype mice; NRF2 KO = NRF2 knockout mice; SCs = mRNA State Changes.

Figure 6

Cellular Stress Network model colored for the WT 1 day cigarette smoke test data set. Red - node corresponds to observed increased mRNA SCs; yellow halo - node is predicted by RCR to have increased activity; blue halo - node is predicted to have decreased activity.