The human body is constantly exposed to endogenous (e.g., mitochondrial reactive oxygen species (ROS) generation, unfolded protein response) and environmental stress. Stressors such as combustion products (diesel exhaust, carbon monoxide, nitrogen oxides, cigarette smoke), particulate matter, ozone, exert a daily challenge to our body's cellular defenses, in particular within the pulmonary and cardiovascular system [1, 2]. Lung epithelial cells directly interface with the external environment and are often the first cells to be exposed to environmental stress [3, 4]. While not facing the external environment directly, cells of the cardiovascular system are constantly exposed to the stressors that circulate in the bloodstream [5–7]. It is therefore not surprising that epidemiological studies have linked exposure to environmental stress to increased incidence of cardiovascular disease over the past decades [8–10]. Thus, further investigation into the mechanistic underpinnings of the response to different types of cellular stress is an important area of human health research [11–14].
One of the central challenges faced by contemporary investigators is how to comprehensively assess the biological impact of complex processes such as the cellular stress response at a molecular level, in order to understand their influence on disease susceptibility and progression. Computational approaches are increasingly being applied to analyze complex biological systems like the cellular stress response, including investigations into the role of key transcription factors such as NRF2 (mediating the antioxidative stress response), or identifying potential mechanisms for how stress can lead to diseases such as asthma [15, 16]. Large scale, systems biology measurements (e.g., transcriptomics, proteomics, and metabolomics) can be applied to molecular regulatory network models in an effort to understand the underlying cellular response to biological insults. The field of pulmonary and cardiovascular biology has been quick to adopt systems biology approaches, using transcriptomic data to investigate the mechanistic basis behind the development of complex, multi-factorial diseases such as atherosclerosis and lung cancer [17–20], particularly with respect to the contribution of CS.
With a view to developing a Systems biology-based risk assessment approach for tobacco products, we are building a series of biological network models that reflect smoking-related molecular changes in the target tissues of the lung and the cardiovascular system. Detailed mechanistic networks are needed to drive the qualitative and eventually quantitative assessment of product-related data (conventional CS and harm reduced next generation products) to determine which pathways are activated in response to such exposures, and to measure the biological impact on in vitro and in vivo systems.
Physiological stress responses are diverse, depending on the type of stressor (chemical or physical), the tissue/cell types affected, and the duration and/or dose of the stressor. Therefore, in order to understand the biological pathways that are affected in response to a particular stressor in a specific physiological context, the availability of comprehensive network models that causally relate the relevant nodes (biological entities or processes) and edges (relationships between nodes) are needed to integrate systems biology data with the current knowledge of biological pathways. Ideally, the impact of environmental stress on all major cellular processes, e.g., proliferation, inflammatory processes, and apoptosis, can be evaluated by integrating multiple biological network models and systems biology data sets, using appropriate computational approaches. We have previously reported on the construction of a network model describing the pathways that are known to regulate cell proliferation in the lung as the first step towards the availability of a publicly available, integrated model of the major cellular processes operating in lung and cardiovascular tissues . However, in order to holistically assess the effects of environmental and endogenous stressors on pulmonary and cardiovascular cells, as well as to link such effects to the onset of related diseases, the availability of detailed mechanistic network models for other major cellular processes is necessary.
Here we report the construction and testing of a more detailed network model reflecting the pathways that are described to operate in response to stress in non-diseased pulmonary and cardiovascular cells. Containing connectivity support from 428 unique literature sources, the network model conveys mechanistic detail about the pathways that are involved in response to several prominent pulmonary and cardiovascular cell stressors, including exogenous factors (i.e., air pollution, environmental toxicants) and endogenous factors (i.e., respiratory chain generated ROS, the unfolded/misfolded proteins). Model content boundaries were set to constrain the coverage of the network model to the stressors and stress responses that can occur in healthy, non-diseased cells of the pulmonary and cardiovascular systems. After establishing these content boundaries, we constructed a literature model of these processes. Next, we used computational analysis of four transcriptomic data sets to identify conserved sub-networks that are activated in response to different stressors, populating the network model with additional nodes and edges in the process.
Towards a verification of the network model, its descriptive content has to be assessed for correctness and relevance; therefore, the network model was evaluated for its ability to detect stress responses to a stressor that was not used to build the network model. Cigarette smoke (CS) contains thousands of chemicals that collectively induce complex molecular responses making CS an ideal test substance. The cellular response to stress induced by CS has been shown to be largely mediated by the oxidative-stress responsive transcription factor NFE2l2 (nuclear factor, erythroid derived 2, like 2; NRF2) making an NRF2 knockout mouse an ideal system to differentiate the response to stress using this network model [22, 23]. Therefore, we tested the ability of the network model to detect cellular stress using transcriptomic data from mouse lung following acute in vivo CS exposure. In addition, we used the network model to investigate the response to acute CS exposure in mice that were constitutively deficient for NRF2. Our results suggest that the use of focused biological network models combined with large scale systems biology data sets can identify the salient biology underlying complex stressors like CS.