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

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.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL)

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.

Database URL:http://wiki.openbel.org/display/BIOC/Datasets

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

BACKGROUND

In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights.

RESULTS

We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes.

CONCLUSIONS

We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the immediate influence zone of epicenters and provide a summary of dysregulated genes, facilitating quick biological analysis. We demonstrate its efficacy on two datasets with differing characteristics, highlighting its general applicability. We also show that EpiTracer is not sensitive to minor changes in the network. The source code for EpiTracer is provided at Github ( https://github.com/narmada26/EpiTracer ).

Systems Toxicology Assessment of the Biological Impact of a Candidate Modified Risk Tobacco Product on Human Organotypic Oral Epithelial Cultures

Cigarette smoke (CS) has been reported to increase predisposition to oral cancer and is also recognized as a risk factor for many conditions including periodontal diseases, gingivitis, and other benign mucosal disorders. Smoking cessation remains the most effective approach for minimizing the risk of smoking-related diseases. However, reduction of harmful constituents by heating rather than combusting tobacco, without modifying the amount of nicotine, is a promising new paradigm in harm reduction. In this study, we compared effects of exposure to aerosol derived from a candidate modified risk tobacco product, the tobacco heating system (THS) 2.2, with those of CS generated from the 3R4F reference cigarette. Human organotypic oral epithelial tissue cultures (EpiOral, MatTek Corporation) were exposed for 28 min to 3R4F CS or THS2.2 aerosol, both diluted with air to comparable nicotine concentrations (0.32 or 0.51 mg nicotine/L aerosol/CS for 3R4F and 0.31 or 0.46 mg/L for THS2.2). We also tested one higher concentration (1.09 mg/L) of THS2.2. A systems toxicology approach was employed combining cellular assays (i.e., cytotoxicity and cytochrome P450 activity assays), comprehensive molecular investigations of the buccal epithelial transcriptome (mRNA and miRNA) by means of computational network biology, measurements of secreted proinflammatory markers, and histopathological analysis. We observed that the impact of 3R4F CS was greater than THS2.2 aerosol in terms of cytotoxicity, morphological tissue alterations, and secretion of inflammatory mediators. Analysis of the transcriptomic changes in the exposed oral cultures revealed significant perturbations in various network models such as apoptosis, necroptosis, senescence, xenobiotic metabolism, oxidative stress, and nuclear factor (erythroid-derived 2)-like 2 (NFE2L2) signaling. The stress responses following THS2.2 aerosol exposure were markedly decreased, and the exposed cultures recovered more completely compared with those exposed to 3R4F CS.

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.

A framework for in vitro systems toxicology assessment of e-liquids

Various electronic nicotine delivery systems (ENDS), of which electronic cigarettes (e-cigs) are the most recognized prototype, have been quickly gaining ground on conventional cigarettes because they are perceived as less harmful. Research assessing the potential effects of ENDS exposure in humans is currently limited and inconclusive. New products are emerging with numerous variations in designs and performance parameters within and across brands. Acknowledging these challenges, we present here a proposed framework for an in vitro systems toxicology assessment of e-liquids and their aerosols, intended to complement the battery of assays for standard toxicity assessments. The proposed framework utilizes high-throughput toxicity assessments of e-liquids and their aerosols, in which the device-to-device variability is minimized, and a systems-level investigation of the cellular mechanisms of toxicity is an integral part. An analytical chemistry investigation is also included as a part of the framework to provide accurate and reliable chemistry data solidifying the toxicological assessment. In its simplest form, the framework comprises of three main layers: (1) high-throughput toxicity screening of e-liquids using primary human cell culture systems; (2) toxicity-related mechanistic assessment of selected e-liquids, and (3) toxicity-related mechanistic assessment of their aerosols using organotypic air-liquid interface airway culture systems. A systems toxicology assessment approach is leveraged to enable in-depth analyses of the toxicity-related cellular mechanisms of e-liquids and their aerosols. We present example use cases to demonstrate the suitability of the framework for a robust in vitro assessment of e-liquids and their aerosols.

Effects of Cigarette Smoke, Cessation, and Switching to Two Heat-Not-Burn Tobacco Products on Lung Lipid Metabolism in C57BL/6 and Apoe-/- Mice-An Integrative Systems Toxicology Analysis

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.

An 8-Month Systems Toxicology Inhalation/Cessation Study in Apoe-/- Mice to Investigate Cardiovascular and Respiratory Exposure Effects of a Candidate Modified Risk Tobacco Product, THS 2.2, Compared With Conventional Cigarettes

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.

Systems toxicology-based assessment of the candidate modified risk tobacco product THS2.2 for the adhesion of monocytic cells to human coronary arterial endothelial cells

Alterations of endothelial adhesive properties by cigarette smoke (CS) can progressively favor the development of atherosclerosis which may cause cardiovascular disorders. Modified risk tobacco products (MRTPs) are tobacco products developed to reduce smoking-related risks. A systems biology/toxicology approach combined with a functional in vitro adhesion assay was used to assess the impact of a candidate heat-not-burn technology-based MRTP, Tobacco Heating System (THS) 2.2, on the adhesion of monocytic cells to human coronary arterial endothelial cells (HCAECs) compared with a reference cigarette (3R4F). HCAECs were treated for 4h with conditioned media of human monocytic Mono Mac 6 (MM6) cells preincubated with low or high concentrations of aqueous extracts from THS2.2 aerosol or 3R4F smoke for 2h (indirect treatment), unconditioned media (direct treatment), or fresh aqueous aerosol/smoke extracts (fresh direct treatment). Functional and molecular investigations revealed that aqueous 3R4F smoke extract promoted the adhesion of MM6 cells to HCAECs via distinct direct and indirect concentration-dependent mechanisms. Using the same approach, we identified significantly reduced effects of aqueous THS2.2 aerosol extract on MM6 cell-HCAEC adhesion, and reduced molecular changes in endothelial and monocytic cells. Ten- and 20-fold increased concentrations of aqueous THS2.2 aerosol extract were necessary to elicit similar effects to those measured with 3R4F in both fresh direct and indirect exposure modalities, respectively. Our systems toxicology study demonstrated reduced effects of an aqueous aerosol extract from the candidate MRTP, THS2.2, using the adhesion of monocytic cells to human coronary endothelial cells as a surrogate pathophysiologically relevant event in atherogenesis.

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.

Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: https://bitbucket.org/akittas/biosubg.

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.

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 7-month cigarette smoke inhalation study in C57BL/6 mice demonstrates reduced lung inflammation and emphysema following smoking cessation or aerosol exposure from a prototypic modified risk tobacco product

Modified risk tobacco products (MRTP) are designed to reduce smoking-related health risks. A murine model of chronic obstructive pulmonary disease (COPD) was applied to investigate classical toxicology end points plus systems toxicology (transcriptomics and proteomics). C57BL/6 mice were exposed to conventional cigarette smoke (3R4F), fresh air (sham), or a prototypic MRTP (pMRTP) aerosol for up to 7 months, including a cessation group and a switching-to-pMRTP group (2 months of 3R4F exposure followed by fresh air or pMRTP for up to 5 months respectively). 3R4F smoke induced the typical adaptive changes in the airways, as well as inflammation in the lung, associated with emphysematous changes (impaired pulmonary function and alveolar damage). At nicotine-matched exposure concentrations of pMRTP aerosol, no signs of lung inflammation and emphysema were observed. Both the cessation and switching groups showed a similar reversal of inflammatory responses and no progression of initial emphysematous changes. A significant impact on biological processes, including COPD-related inflammation, apoptosis, and proliferation, was identified in 3R4F-exposed, but not in pMRTP-exposed lungs. Smoking cessation or switching reduced these perturbations to near sham-exposed levels. In conclusion, the mouse model indicated retarded disease progression upon cessation or switching to pMRTP which alone had no adverse effects.

Systems Toxicology: The Future of Risk Assessment

Risk assessment, in the context of public health, is the process of quantifying the probability of a harmful effect to individuals or populations from human activities. With increasing public health concern regarding the potential risks associated with chemical exposure, there is a need for more predictive and accurate approaches to risk assessment. Developing such an approach requires a mechanistic understanding of the process by which xenobiotic substances perturb biological systems and lead to toxicity. Supplementing the shortfalls of traditional risk assessment with mechanistic biological data has been widely discussed but not routinely implemented in the evaluation of chemical exposure. These mechanistic approaches to risk assessment have been generally referred to as systems toxicology. This Symposium Overview article summarizes 4 talks presented at the 35th Annual Meeting of the American College of 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.

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.