Towards the development of a systems biology-based risk assessment approach for environmental toxicants, including tobacco products in a systems toxicology setting such as the "21st Century Toxicology", we are building a series of computable biological network models specific to non-diseased pulmonary and cardiovascular cells/tissues which capture the molecular events that can be activated following exposure to environmental toxicants. Here we extend on previous work and report on the construction and evaluation of a mechanistic network model focused on DNA damage response and the four main cellular fates induced by stress: autophagy, apoptosis, necroptosis, and senescence. In total, the network consists of 34 sub-models containing 1052 unique nodes and 1538 unique edges which are supported by 1231 PubMed-referenced literature citations. Causal node-edge relationships are described using the Biological Expression Language (BEL), which allows for the semantic representation of life science relationships in a computable format. The Network is provided in .XGMML format and can be viewed using freely available network visualization software, such as Cytoscape.

Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of inflammatory responses. These responses are especially pronounced in the lung, where pulmonary cells sit at the interface between the body's internal and external environments. We combined a literature survey with a computational analysis of multiple transcriptomic data sets to construct a computable causal network model (the Inflammatory Process Network (IPN)) of the main pulmonary inflammatory processes. The IPN model predicted decreased epithelial cell barrier defenses and increased mucus hypersecretion in human bronchial epithelial cells, and an attenuated pro-inflammatory (M1) profile in alveolar macrophages following exposure to CS, consistent with prior results. The IPN provides a comprehensive framework of experimentally supported pathways related to CS-induced pulmonary inflammation. The IPN is freely available to the scientific community as a resource with broad applicability to study the pathogenesis of pulmonary disease.

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.

High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression. This fundamental observation motivated the development of a novel multi-array normalization method based on controllable assumptions, which uses the spike-in control probes to adjust the measured intensities across arrays.

The A/J mouse is highly susceptible to lung tumor induction and has been widely used as a screening model in carcinogenicity testing and chemoprevention studies. However, the A/J mouse model has several disadvantages. Most notably, it develops lung tumors spontaneously. Moreover, there is a considerable gap in our understanding of the underlying mechanisms of pulmonary chemical carcinogenesis in the A/J mouse. Therefore, we examined the differences between spontaneous and cigarette smoke-related lung tumors in the A/J mouse model using mRNA and microRNA (miRNA) profiling. Male A/J mice were exposed whole-body to mainstream cigarette smoke (MS) for 18 months. Gene expression interaction term analysis of lung tumors and surrounding non-tumorous parenchyma samples from animals that were exposed to either 300 mg/m(3) MS or sham-exposed to fresh air indicated significant differential expression of 296 genes. Ingenuity Pathway Analysis(®) (IPA(®)) indicated an overall suppression of the humoral immune response, which was accompanied by a disruption of sphingolipid and glycosaminoglycan metabolism and a deregulation of potentially oncogenic miRNA in tumors of MS-exposed A/J mice. Thus, we propose that MS exposure leads to severe perturbations in pathways essential for tumor recognition by the immune system, thereby potentiating the ability of tumor cells to escape from immune surveillance. Further, exposure to MS appeared to affect expression of miRNA, which have previously been implicated in carcinogenesis and are thought to contribute to tumor progression. Finally, we identified a 50-gene expression signature and show its utility in distinguishing between cigarette smoke-related and spontaneous lung tumors.

Smoking cigarettes is a major risk factor in the development and progression of cardiovascular disease (CVD) and chronic obstructive pulmonary disease (COPD). Modified risk tobacco products (MRTPs) are being developed to reduce smoking-related health risks. The goal of this study was to investigate hallmarks of COPD and CVD over an 8-month period in apolipoprotein E-deficient mice exposed to conventional cigarette smoke (CS) or to the aerosol of a candidate MRTP, tobacco heating system (THS) 2.2. In addition to chronic exposure, cessation or switching to THS2.2 after 2 months of CS exposure was assessed. Engaging a systems toxicology approach, exposure effects were investigated using physiology and histology combined with transcriptomics, lipidomics, and proteomics. CS induced nasal epithelial hyperplasia and metaplasia, lung inflammation, and emphysematous changes (impaired pulmonary function and alveolar damage). Atherogenic effects of CS exposure included altered lipid profiles and aortic plaque formation. Exposure to THS2.2 aerosol (nicotine concentration matched to CS, 29.9 mg/m(3)) neither induced lung inflammation or emphysema nor did it consistently change the lipid profile or enhance the plaque area. Cessation or switching to THS2.2 reversed the inflammatory responses and halted progression of initial emphysematous changes and the aortic plaque area. Biological processes, including senescence, inflammation, and proliferation, were significantly impacted by CS but not by THS2.2 aerosol. Both, cessation and switching to THS2.2 reduced these perturbations to almost sham exposure levels. In conclusion, in this mouse model cessation or switching to THS2.2 retarded the progression of CS-induced atherosclerotic and emphysematous changes, while THS2.2 aerosol alone had minimal adverse effects.

Smoking is associated with several serious diseases, such as lung cancer and chronic obstructive pulmonary disease (COPD). Within our systems toxicology framework, we are assessing whether potential modified risk tobacco products (MRTP) can reduce smoking-related health risks compared to conventional cigarettes. In this article, we evaluated to what extent 2D-PAGE/MALDI MS/MS (2D-PAGE) can complement the iTRAQ LC-MS/MS results from a previously reported mouse inhalation study, in which we assessed a prototypic MRTP (pMRTP). Selected differentially expressed proteins identified by both LC-MS/MS and 2D-PAGE approaches were further verified using reverse-phase protein microarrays. LC-MS/MS captured the effects of cigarette smoke (CS) on the lung proteome more comprehensively than 2D-PAGE. However, an integrated analysis of both proteomics data sets showed that 2D-PAGE data complement the LC-MS/MS results by supporting the overall trend of lower effects of pMRTP aerosol than CS on the lung proteome. Biological effects of CS exposure supported by both methods included increases in immune-related, surfactant metabolism, proteasome, and actin cytoskeleton protein clusters. Overall, while 2D-PAGE has its value, especially as a complementary method for the analysis of effects on intact proteins, LC-MS/MS approaches will likely be the method of choice for proteome analysis in systems toxicology investigations.

Smoking of combustible cigarettes has a major impact on human health. Using a systems toxicology approach in a model of chronic obstructive pulmonary disease (C57BL/6 mice), we assessed the health consequences in mice of an aerosol derived from a prototype modified risk tobacco product (pMRTP) as compared to conventional cigarettes. We investigated physiological and histological endpoints in parallel with transcriptomics, lipidomics, and proteomics profiles in mice exposed to a reference cigarette (3R4F) smoke or a pMRTP aerosol for up to 7 months. We also included a cessation group and a switching-to-pMRTP group (after 2 months of 3R4F exposure) in addition to the control (fresh air-exposed) group, to understand the potential risk reduction of switching to pMRTP compared with continuous 3R4F exposure and cessation. The present manuscript describes the study design, setup, and implementation, as well as the generation, processing, and quality control analysis of the toxicology and 'omics' datasets that are accessible in public repositories for further analyses.

The impact of cigarette smoke (CS), a major cause of lung diseases, on the composition and metabolism of lung lipids is incompletely understood. Here, we integrated quantitative lipidomics and proteomics to investigate exposure effects on lung lipid metabolism in a C57BL/6 and an Apolipoprotein E-deficient (Apoe(-/-)) mouse study. In these studies, mice were exposed to high concentrations of 3R4F reference CS, aerosol from potential modified risk tobacco products (MRTPs) or filtered air (Sham) for up to 8 months. The 2 assessed MRTPs, the prototypical MRTP for C57BL/6 mice and the Tobacco Heating System 2.2 for Apoe(-/-) mice, utilize "heat-not-burn" technologies and were each matched in nicotine concentrations to the 3R4F CS. After 2 months of CS exposure, some groups were either switched to the MRTP or underwent cessation. In both mouse strains, CS strongly affected several categories of lung lipids and lipid-related proteins. Candidate surfactant lipids, surfactant proteins, and surfactant metabolizing proteins were increased. Inflammatory eicosanoids, their metabolic enzymes, and several ceramide classes were elevated. Overall, CS induced a coordinated lipid response controlled by transcription regulators such as SREBP proteins and supported by other metabolic adaptations. In contrast, most of these changes were absent in the mice exposed to the potential MRTPs, in the cessation group, and the switching group. Our findings demonstrate the complex biological response of the lungs to CS exposure and support the benefits of cessation or switching to a heat-not-burn product using a design such as those employed in this study.

Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of "omics" data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.

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.

The use of flavoring substances is an important element in the development of reduced-risk products for adult smokers to increase product acceptance and encourage switching from cigarettes. In a first step towards characterizing the sub-chronic inhalation toxicity of neat flavoring substances, a study was conducted using a mixture of the substances in a base solution of e-liquid, where the standard toxicological endpoints of the nebulized aerosols were supplemented with transcriptomics analysis. The flavor mixture was produced by grouping 178 flavors into 26 distinct chemical groups based on structural similarities and potential metabolic and biological effects. Flavoring substances predicted to show the highest toxicological effect from each group were selected as the flavor group representatives (FGR). Following Organization for Economic Cooperation and Development Testing Guideline 413, rats were exposed to three concentrations of the FGR mixture in an e-liquid composed of nicotine (23 µg/L), propylene glycol (1520 µg/L), and vegetable glycerin (1890 µg/L), while non-flavored and no-nicotine mixtures were included as references to identify potential additive or synergistic effects between nicotine and the flavoring substances. The results indicated that the inhalation of an e-liquid containing the mixture of FGRs caused very minimal local and systemic toxic effects. In particular, there were no remarkable clinical (in-life) observations in flavored e-liquid-exposed rats. The biological effects related to exposure to the mixture of neat FGRs were limited and mainly nicotine-mediated, including changes in hematological and blood chemistry parameters and organ weight. These results indicate no significant additive biological changes following inhalation exposure to the nebulized FGR mixture above the nicotine effects measured in this sub-chronic inhalation study. In a subsequent study, e-liquids with FGR mixtures will be aerosolized by thermal treatment and assessed for toxicity.

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

Bidirectional correlations between cigarette smoking and affective disorders, such as depression, anxiety, and schizophrenia, are well documented. These findings have led to substantial investigations into the effects of the major tobacco alkaloid, nicotine, and to a lesser extent, of other tobacco constituents, on the central nervous system (CNS). However, systematic profiling of the neuropharmacological effects of tobacco constituents is limited. To elucidate the effects of selected tobacco constituents on the CNS, we used the SmartCube® system, which captures and classifies behavioral features of compound-treated mice, to profile the psychiatric drugs-like properties of previously reported neuroactive tobacco compounds in mice. Daily intraperitoneal injection of nicotine (0.5 and 1 mg/kg/day) and anatabine (5 mg/kg/day) for 7 days produced antidepressant-like behavioral SmartCube® signatures in mice, and these results were supported by the improved active coping responses in the forced swim tests. Conversely, ferulic acid did not show any identifiable class signatures in the SmartCube® tests, but rather displayed subclass signatures associated with acetylcholinesterase inhibitors. In novel object recognition memory test in rats, ferulic acid improved memory after 7 days of subcutaneous injection at 0.3 or 3 mg/kg/day. These results support previous findings showing the antidepressant drug-like effects of nicotine and the nootropic effects of ferulic acid. This is also the first report on the antidepressant drug-like effects of anatabine in rodents. This study provides a systemic behavioral evaluation of tobacco alkaloids and further insights into the association between affective disorders and smoking incidence.