A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Protective Effects of Quercetin on Livers from Mice Exposed to Long-Term Cigarette Smoke

Cigarette smoke is highly toxic, and it can promote increased production of reactive species and inflammatory response and leads to liver diseases. Quercetin is a flavonoid that displays antioxidant and anti-inflammatory activities in liver diseases. This study aimed at evaluating the protective effects of quercetin on livers from mice exposed to long-term cigarette smoke exposure. Male C57BL/6 mice were divided into five groups: control (CG), vehicle (VG), quercetin (QG), cigarette smoke (CSG), quercetin, and cigarette smoke (QCSG). CSG and QCSG were exposed to cigarette smoke for sixty consecutive days; at the end of the exposures, all animals were euthanized. Mice that received quercetin daily and were exposed to cigarette smoke showed a reduced influx of inflammatory cells, oxidative stress, inflammatory reaction, and histopathological changes in the liver, compared to CSG. These results suggest that quercetin may be an effective adjuvant for treating damage to the liver due to cigarette smoke exposure.

NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks

Background

High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data can be guided based on a priori knowledge, such as networks of related proteins or genes. Assessing the response of a specific mechanism and investigating its biological basis is extremely important in systems toxicology; as compounds or treatment need to be assessed with respect to a predefined set of key mechanisms that could lead to toxicity. Two-layer networks are suitable for this task, and a robust computational methodology specifically addressing those needs was previously published.

The NPA package (https://github.com/philipmorrisintl/NPA) implements the algorithm, and a data package of eight two-layer networks representing key mechanisms, such as xenobiotic metabolism, apoptosis, or epithelial immune innate activation, is provided.

Results

Gene expression data from an animal study are analyzed using the package and its network models. The functionalities are implemented using R6 classes, making the use of the package seamless and intuitive. The various network responses are analyzed using the leading node analysis, and an overall perturbation, called the Biological Impact Factor, is computed.

Conclusions

The NPA package implements the published network perturbation amplitude methodology and provides a set of two-layer networks encoded in the Biological Expression Language.

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract

Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways’ defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary beating, and (3) goblet cell hyperplasia/metaplasia and that were built in the Biological Expression Language, which is both human-readable and computable. The network analysis of highly connected nodes and pathways demonstrated that the relevant biology was captured in the MCC models. We also show the scoring of transcriptomic data onto these network models and demonstrate that the models capture the perturbation in each dataset accurately. This work is a continuation of our approach to use computational biological network models and mathematical algorithms that allow for the interpretation of high-throughput molecular datasets in the context of known biology. The MCC network model suite can be a valuable tool in personalized medicine to further understand heterogeneity and individual drug responses in complex respiratory diseases.

Taurine treatment decreases inflammation and oxidative stress in lungs of adult mice exposed to cigarette smoke

Taurine is the major free amino acid found in mammalian cells and is known to be an antioxidant and membrane-stabilizing agent. This study aimed to evaluate the effects of taurine on oxidative stress and inflammatory response in the lungs of mice exposed to cigarette smoke. Fifty male C57BL/6 mice were divided into 5 groups: control group (CG), vehicle group (VG), taurine group (TG), cigarette smoke group (CSG), and cigarette smoke + taurine group (CSTG). For five consecutive days, CSG and CSTG were exposed to 4 cigarettes 3 times a day. Taurine administration was able to reduce total leukocytes in bronchoalveolar lavage fluid in CSTG compared to CSG. There was an increase in antioxidant superoxide dismutase and catalase activity in CSG compared to that in CG and TG, and a decrease in CSTG compared to CSG. There was an increase in the concentration of TNF and IL-17 in CSG and CSTG compared to CG and TG. There was an increase in the concentration of IL-22 in CSG compared to CG and TG, and a decrease in CSTG compared to CSG. The administration of taurine has been shown to reduce the inflammation and oxidative stress induced by short-term exposure to cigarette smoke.

A 90-day OECD TG 413 rat inhalation study with systems toxicology endpoints demonstrates reduced exposure effects of the aerosol from the carbon heated tobacco product version 1.2 (CHTP1.2) compared with cigarette smoke. II. Systems toxicology assessment

Modified risk tobacco products (MRTPs) have the potential to reduce smoking-related health risks. The Carbon Heated Tobacco Product 1.2 (CHTP1.2) is a potential MRTP that uses a pressed carbon heat source to generate an aerosol by heating tobacco. Here, we report the results from the systems toxicology arm of a 90-day rat inhalation study (OECD test guideline 413) to assess the effects of CHTP1.2 aerosol compared with cigarette smoke (CS). Transcriptomics, proteomics, and lipidomics analyses complemented the standard endpoints. In the respiratory nasal epithelium, CS induced an adaptive tissue and inflammatory response, which was much weaker after CHTP1.2 aerosol exposure, mostly limited to the highest CHTP1.2 concentration (at twice the 3R4F CS concentration: 50 vs. 23 μg nicotine/L), in female rats. In the lungs, the effects of CS exposure included inflammatory and cellular stress responses, which were absent or much lower after CHTP1.2 aerosol exposure. Outside of the respiratory tract, CS and CHTP1.2 aerosol induced effects that were previously associated with exposure to any nicotine-containing aerosol, e.g., lower lipid concentrations in serum. Overall, this systems toxicology analysis complements and confirms the results from classical toxicological endpoints and further suggests potentially reduced respiratory health risks of CHTP1.2.

Systems toxicology meta-analysis of in vitro assessment studies: biological impact of a candidate modified-risk tobacco product aerosol compared with cigarette smoke on human organotypic cultures of the aerodigestive tract

Systems biology combines comprehensive molecular analyses with quantitative modeling to understand the characteristics of a biological system as a whole. Leveraging a similar approach, systems toxicology aims to decipher complex biological responses following exposures. This work reports a systems toxicology meta-analysis in the context of in vitro assessment of a candidate modified-risk tobacco product (MRTP) using three human organotypic cultures of the aerodigestive tract (buccal, bronchial, and nasal epithelia). Complementing a series of functional measures, a causal network enrichment analysis of transcriptomic data was used to compare quantitatively the biological impact of aerosol from the Tobacco Heating System (THS) 2.2, a candidate MRTP, with 3R4F cigarette smoke (CS) at similar nicotine concentrations. Lower toxicity was observed in all cultures following exposure to THS2.2 aerosol compared with 3R4F CS. Because of their morphological differences, a smaller exposure impact was observed in the buccal (stratified epithelium) compared with the bronchial and nasal (pseudostratified epithelium). However, the causal network enrichment approach supported a similar mechanistic impact of CS across the three cultures, including the impact on xenobiotic, oxidative stress, and inflammatory responses. At comparable nicotine concentrations, THS2.2 aerosol elicited reduced and more transient effects on these processes. To demonstrate the benefits of additional data modalities, we employed a newly established targeted mass-spectrometry marker panel to further confirm the reduced cellular stress responses elicited by THS2.2 aerosol compared with 3R4F CS in the nasal culture. Overall, this work demonstrates the applicability and robustness of the systems toxicology approach for in vitro inhalation toxicity assessment.

Novel approaches to develop community-built biological network models for potential drug discovery

INTRODUCTION

Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

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.

Evaluation of the Tobacco Heating System 2.2 (THS2.2). Part 5: microRNA expression from a 90-day rat inhalation study indicates that exposure to THS2.2 aerosol causes reduced effects on lung tissue compared with cigarette smoke

Modified-risk tobacco products (MRTP) are designed to reduce the individual risk of tobacco-related disease as well as population harm compared to smoking cigarettes. Experimental proof of their benefit needs to be provided at multiple levels in research fields. Here, we examined microRNA (miRNA) levels in the lungs of rats exposed to a candidate modified-risk tobacco product, the Tobacco Heating System 2.2 (THS2.2) in a 90-day OECD TG-413 inhalation study. Our aim was to assess the miRNA response to THS2.2 aerosol compared with the response to combustible cigarettes (CC) smoke from the reference cigarette 3R4F. CC smoke exposure, but not THS2.2 aerosol exposure, caused global miRNA downregulation, which may be explained by the interference of CC smoke constituents with the miRNA processing machinery. Upregulation of specific miRNA species, such as miR-146a/b and miR-182, indicated that they are causal elements in the inflammatory response in CC-exposed lungs, but they were reduced after THS2.2 aerosol exposure. Transforming transcriptomic data into protein activity based on corresponding downstream gene expression, we identified potential mechanisms for miR-146a/b and miR-182 that were activated by CC smoke but not by THS2.2 aerosol and possibly involved in the regulation of those miRNAs. The inclusion of miRNA profiling in systems toxicology approaches increases the mechanistic understanding of the complex exposure responses.

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.

Biological impact of cigarette smoke compared to an aerosol produced from a prototypic modified risk tobacco product on normal human bronchial epithelial cells

Cigarette smoking causes serious and fatal diseases. The best way for smokers to avoid health risks is to quit smoking. Using modified risk tobacco products (MRTPs) may be an alternative to reduce the harm caused for those who are unwilling to quit smoking, but little is known about the toxic effects of MRTPs, nor were the molecular mechanisms of toxicity investigated in detail. The toxicity of an MRTP and the potential molecular mechanisms involved were investigated in high-content screening tests and whole genome transcriptomics analyses using human bronchial epithelial cells. The prototypic (p)MRTP that was tested had less impact than reference cigarette 3R4F on the cellular oxidative stress response and cell death pathways. Higher pMRTP aerosol extract concentrations had impact on pathways associated with the detoxification of xenobiotics and the reduction of oxidative damage. A pMRTP aerosol concentration up to 18 times higher than the 3R4F caused similar perturbation effects in biological networks and led to the perturbation of networks related to cell stress, and proliferation biology. These results may further facilitate the development of a systems toxicology-based impact assessment for use in future risk assessments in line with the 21st century toxicology paradigm, as shown here for an MRTP.

Impact Assessment of Cigarette Smoke Exposure on Organotypic Bronchial Epithelial Tissue Cultures: A Comparison of Mono-Culture and Coculture Model Containing Fibroblasts

Organotypic 3D cultures of epithelial cells are grown at the air-liquid interface (ALI) and resemble the in vivo counterparts. Although the complexity of in vivo cellular responses could be better manifested in coculture models in which additional cell types such as fibroblasts were incorporated, the presence of another cell type could mask the response of the other. This study reports the impact of whole cigarette smoke (CS) exposure on organotypic mono- and coculture models to evaluate the relevancy of organotypic models for toxicological assessment of aerosols. Two organotypic bronchial models were directly exposed to low and high concentrations of CS of the reference research cigarette 3R4F: monoculture of bronchial epithelial cells without fibroblasts (BR) and coculture with fibroblasts (BRF) models. Adenylate kinase (AK)-based cytotoxicity, cytochrome P450 (CYP) 1A1/1B1 activity, tissue histology, and concentrations of secreted mediators into the basolateral media, as well as transcriptomes were evaluated following the CS exposure. The results demonstrated similar impact of CS on the AK-based cytotoxicity, CYP1A1/1B1 activity, and tissue histology in both models. However, a greater number of secreted mediators was identified in the basolateral media of the monoculture than in the coculture models. Furthermore, annotation analysis and network-based systems biology analysis of the transcriptomic profiles indicated a more prominent cellular stress and tissue damage following CS in the monoculture epithelium model without fibroblasts. Finally, our results indicated that an in vivo smoking-induced xenobiotic metabolism response of bronchial epithelial cells was better reflected from the in vitro CS-exposed coculture model.

Toxicity of aerosols of nicotine and pyruvic acid (separate and combined) in Sprague-Dawley rats in a 28-day OECD 412 inhalation study and assessment of systems toxicology

Toxicity of nebulized nicotine (Nic) and nicotine/pyruvic acid mixtures (Nic/Pyr) was characterized in a 28-day Organization for Economic Co-operation and Development 412 inhalation study with additional transcriptomic and lipidomic analyses. Sprague-Dawley rats were nose-only exposed, 6 h/day, 5 days/week to filtered air, saline, nicotine (50 µg/l), sodium pyruvate (NaPyr, 33.9 µg/l) or equimolar Nic/Pyr mixtures (18, 25 and 50 µg nicotine/l). Saline and NaPyr caused no health effects, but rats exposed to nicotine-containing aerosols had decreased body weight gains and concentration-dependent increases in liver weight. Blood neutrophil counts were increased and lymphocyte counts decreased in rats exposed to nicotine; activities of alkaline phosphatase and alanine aminotransferase were increased, and levels of cholesterol and glucose decreased. The only histopathologic finding in non-respiratory tract organs was increased liver vacuolation and glycogen content. Respiratory tract findings upon nicotine exposure (but also some phosphate-buffered saline aerosol effects) were observed only in the larynx and were limited to adaptive changes. Gene expression changes in the lung and liver were very weak. Nic and Nic/Pyr caused few significant changes (including Cyp1a1 gene upregulation). Changes were predominantly related to energy metabolism and fatty acid metabolism but did not indicate an obvious toxicity-related response. Nicotine exposure lowered plasma lipids, including cholesteryl ester (CE) and free cholesterol and, in the liver, phospholipids and sphingolipids. Nic, NaPyr and Nic/Pyr decreased hepatic triacylglycerol and CE. In the lung, Nic and Nic/Pyr increased CE levels. These data suggest that only minor biologic effects related to inhalation of Nic or Nic/Pyr aerosols were observed in this 28-day study.

Transcriptional profiling and targeted proteomics reveals common molecular changes associated with cigarette smoke-induced lung emphysema development in five susceptible mouse strains

BACKGROUND

Mouse models are useful for studying cigarette smoke (CS)-induced chronic pulmonary pathologies such as lung emphysema. To enhance translation of large-scale omics data from mechanistic studies into pathophysiological changes, we have developed computational tools based on reverse causal reasoning (RCR).

OBJECTIVE

In the present study we applied a systems biology approach leveraging RCR to identify molecular mechanistic explanations of pathophysiological changes associated with CS-induced lung emphysema in susceptible mice.

METHODS

The lung transcriptomes of five mouse models (C57BL/6, ApoE (-/-) , A/J, CD1, and Nrf2 (-/-) ) were analyzed following 5-7 months of CS exposure.

RESULTS

We predicted 39 molecular changes mostly related to inflammatory processes including known key emphysema drivers such as NF-κB and TLR4 signaling, and increased levels of TNF-α, CSF2, and several interleukins. More importantly, RCR predicted potential molecular mechanisms that are less well-established, including increased transcriptional activity of PU.1, STAT1, C/EBP, FOXM1, YY1, and N-COR, and reduced protein abundance of ITGB6 and CFTR. We corroborated several predictions using targeted proteomic approaches, demonstrating increased abundance of CSF2, C/EBPα, C/EBPβ, PU.1, BRCA1, and STAT1.

CONCLUSION

These systems biology-derived candidate mechanisms common to susceptible mouse models may enhance understanding of CS-induced molecular processes underlying emphysema development in mice and their relevancy for human chronic obstructive pulmonary disease.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

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

A systems biology approach reveals the dose- and time-dependent effect of primary human airway epithelium tissue culture after exposure to cigarette smoke in vitro

To establish a relevant in vitro model for systems toxicology-based mechanistic assessment of environmental stressors such as cigarette smoke (CS), we exposed human organotypic bronchial epithelial tissue cultures at the air liquid interface (ALI) to various CS doses. Previously, we compared in vitro gene expression changes with published human airway epithelia in vivo data to assess their similarities. Here, we present a follow-up evaluation of these in vitro transcriptomics data, using complementary computational approaches and an integrated mRNA-microRNA (miRNA) analysis. The main cellular pathways perturbed by CS exposure were related to stress responses (oxidative stress and xenobiotic metabolism), inflammation (inhibition of nuclear factor-κB and the interferon gamma-dependent pathway), and proliferation/differentiation. Within post-exposure periods up to 48 hours, a transient kinetic response was observed at lower CS doses, whereas higher doses resulted in more sustained responses. In conclusion, this systems toxicology approach has potential for product testing according to "21st Century Toxicology".

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

The response of human nasal and bronchial organotypic tissue cultures to repeated whole cigarette smoke exposure

Exposure to cigarette smoke (CS) is linked to the development of respiratory diseases, and there is a need to understand the mechanisms whereby CS causes damage. Although animal models have provided valuable insights into smoking-related respiratory tract damage, modern toxicity testing calls for reliable in vitro models as alternatives for animal experimentation. We report on a repeated whole mainstream CS exposure of nasal and bronchial organotypic tissue cultures that mimic the morphological, physiological, and molecular attributes of the human respiratory tract. Despite the similar cellular staining and cytokine secretion in both tissue types, the transcriptomic analyses in the context of biological network models identified similar and diverse biological processes that were impacted by CS-exposed nasal and bronchial cultures. Our results demonstrate that nasal and bronchial tissue cultures are appropriate in vitro models for the assessment of CS-induced adverse effects in the respiratory system and promising alternative to animal experimentation.

In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures

Smoking has been associated with diseases of the lung, pulmonary airways and oral cavity. Cytologic, genomic and transcriptomic changes in oral mucosa correlate with oral pre-neoplasia, cancer and inflammation (e.g. periodontitis). Alteration of smoking-related gene expression changes in oral epithelial cells is similar to that in bronchial and nasal epithelial cells. Using a systems toxicology approach, we have previously assessed the impact of cigarette smoke (CS) seen as perturbations of biological processes in human nasal and bronchial organotypic epithelial culture models. Here, we report our further assessment using in vitro human oral organotypic epithelium models. We exposed the buccal and gingival organotypic epithelial tissue cultures to CS at the air-liquid interface. CS exposure was associated with increased secretion of inflammatory mediators, induction of cytochrome P450s activity and overall weak toxicity in both tissues. Using microarray technology, gene-set analysis and a novel computational modeling approach leveraging causal biological network models, we identified CS impact on xenobiotic metabolism-related pathways accompanied by a more subtle alteration in inflammatory processes. Gene-set analysis further indicated that the CS-induced pathways in the in vitro buccal tissue models resembled those in the in vivo buccal biopsies of smokers from a published dataset. These findings support the translatability of systems responses from in vitro to in vivo and demonstrate the applicability of oral organotypical tissue models for an impact assessment of CS on various tissues exposed during smoking, as well as for impact assessment of reduced-risk products.

An algorithm for score aggregation over causal biological networks based on random walk sampling

Background

We recently published in BMC Systems Biology an approach for calculating the perturbation amplitudes of causal network models by integrating gene differential expression data. This approach relies on the process of score aggregation, which combines the perturbations at the level of the individual network nodes into a global measure that quantifies the perturbation of the network as a whole. Such "bottom-up" aggregation relates the changes in molecular entities measured by omics technologies to systems-level phenotypes. However, the aggregation method we used is limited to a specific class of causal network models called "causally consistent", which is equivalent to the notion of balance of a signed graph used in graph theory. As a consequence of this limitation, our aggregation method cannot be used in the many relevant cases involving "causally inconsistent" network models such as those containing negative feedbacks.

Findings

In this note, we propose an algorithm called "sampling of spanning trees" (SST) that extends our published aggregation method to causally inconsistent network models by replacing the signed relationships between the network nodes by an appropriate continuous measure. The SST algorithm is based on spanning trees, which are a particular class of subgraphs used in graph theory, and on a sampling procedure leveraging the properties of specific random walks on the graph. This algorithm is applied to several cases of biological interest.

Conclusions

The SST algorithm provides a practical means of aggregating nodal values over causally inconsistent network models based on solid mathematical foundations. We showed its utility in systems biology, where the nodal values can be perturbation amplitudes of protein activities or gene differential expressions, while the networks can be models of cellular signaling or expression regulation. Since the SST algorithm is based on general graph-theoretical considerations, it is scalable to arbitrary graph sizes and can potentially be used for performing quantitative analyses in any context involving signed graphs.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-0500-7-516) contains supplementary material, which is available to authorized users.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models

BACKGROUND

High-throughput measurement technologies such as microarrays provide complex datasets reflecting mechanisms perturbed in an experiment, typically a treatment vs. control design. Analysis of these information rich data can be guided based on a priori knowledge, such as networks or set of related proteins or genes. Among those, cause-and-effect network models are becoming increasingly popular and more than eighty such models, describing processes involved in cell proliferation, cell fate, cell stress, and inflammation have already been published. A meaningful systems toxicology approach to study the response of a cell system, or organism, exposed to bio-active substances requires a quantitative measure of dose-response at network level, to go beyond the differential expression of single genes.

RESULTS

We developed a method that quantifies network response in an interpretable manner. It fully exploits the (signed graph) structure of cause-and-effect networks models to integrate and mine transcriptomics measurements. The presented approach also enables the extraction of network-based signatures for predicting a phenotype of interest. The obtained signatures are coherent with the underlying network perturbation and can lead to more robust predictions across independent studies. The value of the various components of our mathematically coherent approach is substantiated using several in vivo and in vitro transcriptomics datasets. As a proof-of-principle, our methodology was applied to unravel mechanisms related to the efficacy of a specific anti-inflammatory drug in patients suffering from ulcerative colitis. A plausible mechanistic explanation of the unequal efficacy of the drug is provided. Moreover, by utilizing the underlying mechanisms, an accurate and robust network-based diagnosis was built to predict the response to the treatment.

CONCLUSION

The presented framework efficiently integrates transcriptomics data and "cause and effect" network models to enable a mathematically coherent framework from quantitative impact assessment and data interpretation to patient stratification for diagnosis purposes.

A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability

Background

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.

Methods

We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis.

Results

Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE^-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques.

Conclusions

We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability.

A 28-day rat inhalation study with an integrated molecular toxicology endpoint demonstrates reduced exposure effects for a prototypic modified risk tobacco product compared with conventional cigarettes

Towards a systems toxicology-based risk assessment, we investigated molecular perturbations accompanying histopathological changes in a 28-day rat inhalation study combining transcriptomics with classical histopathology. We demonstrated reduced biological activity of a prototypic modified risk tobacco product (pMRTP) compared with the reference research cigarette 3R4F. Rats were exposed to filtered air or to three concentrations of mainstream smoke (MS) from 3R4F, or to a high concentration of MS from a pMRTP. Histopathology revealed concentration-dependent changes in response to 3R4F that were irritative stress-related in nasal and bronchial epithelium, and inflammation-related in the lung parenchyma. For pMRTP, significant changes were seen in the nasal epithelium only. Transcriptomics data were obtained from nasal and bronchial epithelium and lung parenchyma. Concentration-dependent gene expression changes were observed following 3R4F exposure, with much smaller changes for pMRTP. A computational-modeling approach based on causal models of tissue-specific biological networks identified cell stress, inflammation, proliferation, and senescence as the most perturbed molecular mechanisms. These perturbations correlated with histopathological observations. Only weak perturbations were observed for pMRTP. In conclusion, a correlative evaluation of classical histopathology together with gene expression-based computational network models may facilitate a systems toxicology-based risk assessment, as shown for a pMRTP.

Systems biology approach for evaluating the biological impact of environmental toxicants in vitro

Exposure to cigarette smoke is a leading cause of lung diseases including chronic obstructive pulmonary disease and cancer. Cigarette smoke is a complex aerosol containing over 6000 chemicals and thus it is difficult to determine individual contributions to overall toxicity as well as the molecular mechanisms by which smoke constituents exert their effects. We selected three well-known harmful and potentially harmful constituents (HPHCs) in tobacco smoke, acrolein, formaldehyde and catechol, and established a high-content screening method using normal human bronchial epithelial cells, which are the first bronchial cells in contact with cigarette smoke. The impact of each HPHC was investigated using 13 indicators of cellular toxicity complemented with a microarray-based whole-transcriptome analysis followed by a computational approach leveraging mechanistic network models to identify and quantify perturbed molecular pathways. HPHCs were evaluated over a wide range of concentrations and at different exposure time points (4, 8, and 24 h). By high-content screening, the toxic effects of the three HPHCs could be observed only at the highest doses. Whole-genome transcriptomics unraveled toxicity mechanisms at lower doses and earlier time points. The most prevalent toxicity mechanisms observed were DNA damage/growth arrest, oxidative stress, mitochondrial stress, and apoptosis/necrosis. A combination of multiple toxicological end points with a systems-based impact assessment allows for a more robust scientific basis for the toxicological assessment of HPHCs, allowing insight into time- and dose-dependent molecular perturbations of specific biological pathways. This approach allowed us to establish an in vitro systems toxicology platform that can be applied to a broader selection of HPHCs and their mixtures and can serve more generally as the basis for testing the impact of other environmental toxicants on normal bronchial epithelial cells.

Systems toxicology: from basic research to risk assessment

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.

Systems biology in drug discovery and development

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

On Crowd-verification of Biological Networks

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks.

Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified.

The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

Case study: the role of mechanistic network models in systems toxicology

Twenty first century systems toxicology approaches enable the discovery of biological pathways affected in response to active substances. Here, we briefly summarize current network approaches that facilitate the detailed mechanistic understanding of the impact of a given stimulus on a biological system. We also introduce our network-based method with two use cases and show how causal biological network models combined with computational methods provide quantitative mechanistic insights. Our approach provides a robust comparison of the transcriptional responses in different experimental systems and enables the identification of network-based biomarkers modulated in response to exposure. These advances can also be applied to pharmacology, where the understanding of disease mechanisms and adverse drug effects is imperative for the development of efficient and safe treatment options.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology.

Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset

We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.