Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems

Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States

Virtually any stressor that alters the cellular homeostatic state may result in an inflammatory response. As a critical component of innate immunity, inflammasomes play a prominent role in the inflammatory response. The information on inflammasome biology is rapidly growing, thus creating the need for structuring it into a model that can help visualize and enhance the understanding of underlying biological processes. Causal biological network (CBN) models provide predictive power for novel disease mechanisms and treatment outcomes. We assembled the available literature information on inflammasome activation into the CBN model and scored it with publicly available transcriptomic datasets that address viral infection of the lungs, osteo- and rheumatoid arthritis, psoriasis, and aging. The scoring inferred pathway activation leading to NLRP3 inflammasome activation in these diverse conditions, demonstrating that the CBN model provides a platform for interpreting transcriptomic data in the context of inflammasome activation.

A 7-month inhalation toxicology study in C57BL/6 mice demonstrates reduced pulmonary inflammation and emphysematous changes following smoking cessation or switching to e-vapor products

Cigarette smoking causes serious diseases, including lung cancer, atherosclerotic coronary artery disease, peripheral vascular disease, chronic bronchitis, and emphysema. While cessation remains the most effective approach to minimize smoking-related disease, alternative non-combustible tobacco-derived nicotine-containing products may reduce disease risks among those unable or unwilling to quit. E-vapor aerosols typically contain significantly lower levels of smoke-related harmful and potentially harmful constituents; however, health risks of long-term inhalation exposures are unknown. We designed a 7-month inhalation study in C57BL/6 mice to evaluate long-term respiratory toxicity of e-vapor aerosols compared to cigarette smoke and to assess the impact of smoking cessation (Cessation group) or switching to an e-vapor product (Switching group) after 3 months of exposure to 3R4F cigarette smoke (CS). There were no significant changes in in-life observations (body weights, clinical signs) in e-vapor groups compared to the Sham Control. The 3R4F CS group showed reduced respiratory function during exposure and had lower body weight and showed transient signs of distress post-exposure. Following 7 months of exposure, e-vapor aerosols resulted in no or minimal increase in pulmonary inflammation, while exposure to 3R4F CS led to impairment of lung function and caused marked lung inflammation and emphysematous changes. Biological changes observed in the Switching group were similar to the Cessation group. 3R4F CS exposure dysregulated the lung and nasal tissue transcriptome, while these molecular effects were substantially lower in the e-vapor group. Results from this study demonstrate that in comparison with 3R4F CS, e-vapor aerosols induce substantially lower biological responses including pulmonary inflammation and emphysematous changes, and that complete switching from CS to e-vapor products significantly reduces biological changes associated with CS in C57BL/6 mice.

Mechanistic Evaluation of the Impact of Smoking and Chronic Obstructive Pulmonary Disease on the Nasal Epithelium

Chronic obstructive pulmonary disease (COPD) is one of the major causes of chronic morbidity and mortality worldwide. The development of markers of COPD onset is hampered by the lack of accessibility to the primary target tissue, and there is a need to consider other sample sources as surrogates for biomarker research. Airborne toxicants pass through the nasal epithelium before reaching the lower airways, and the similarity with bronchial histology makes it an attractive surrogate for lower airways. In this work, we describe the transcriptomics findings from the nasal epithelia of subjects enrolled in a clinical study focusing on the identification of COPD biomarkers. Transcriptomic data were analyzed using the biological network approach that enabled us to pinpoint the biological processes affected in the upper respiratory tract in response to smoking and mild-to-moderate COPD. Our results indicated that nasal and lower airway immune responses were considerably different in COPD subjects and caution should be exercised when using upper airway samples as a surrogate for the lower airway. Nevertheless, the network approach described here could present a sensitive means of identifying smokers at risk of developing COPD.

Semi-Automated Curation Allows Causal Network Model Building for the Quantification of Age-Dependent Plaque Progression in ApoE-/- Mouse

The cellular and molecular mechanisms behind the process of atherosclerotic plaque destabilization are complex, and molecular data from aortic plaques are difficult to interpret. Biological network models may overcome these difficulties and precisely quantify the molecular mechanisms impacted during disease progression. The atherosclerosis plaque destabilization biological network model was constructed with the semiautomated curation pipeline, BELIEF. Cellular and molecular mechanisms promoting plaque destabilization or rupture were captured in the network model. Public transcriptomic data sets were used to demonstrate the specificity of the network model and to capture the different mechanisms that were impacted in ApoE^-/- mouse aorta at 6 and 32 weeks. We concluded that network models combined with the network perturbation amplitude algorithm provide a sensitive, quantitative method to follow disease progression at the molecular level. This approach can be used to investigate and quantify molecular mechanisms during plaque progression.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.