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MOTIVATION: The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease etiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets. RESULTS: Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved. AVAILABILITY AND IMPLEMENTATION: The DST source code and Docker images are respectively available at https://github.com/SCAI-BIO/data-steward and https://hub.docker.com/r/phwegner/data-steward. Furthermore, the DST is hosted at https://data-steward.bio.scai.fraunhofer.de/data-steward. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Demência , Semântica , Humanos , Software , Demência/diagnósticoRESUMO
MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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Redes Reguladoras de Genes , Predisposição Genética para Doença , Biologia Computacional , Humanos , Modelos Estatísticos , Pesquisa Translacional BiomédicaRESUMO
Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
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Mineração de Dados , Doenças Neurodegenerativas/genética , Animais , Biologia Computacional , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Bases de Conhecimento , Polimorfismo de Nucleotídeo ÚnicoRESUMO
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.
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Aterosclerose/patologia , Vasos Sanguíneos/patologia , Inflamação/patologia , Modelos Cardiovasculares , Placa Aterosclerótica/patologia , Transdução de Sinais , Animais , Apolipoproteínas E/deficiência , Apolipoproteínas E/metabolismo , Aterosclerose/genética , Análise por Conglomerados , Bases de Dados como Assunto , Humanos , Camundongos , Razão de Chances , Placa Aterosclerótica/genética , Software , Transcriptoma/genética , Pesquisa Translacional BiomédicaRESUMO
Organotypic culture of human primary bronchial epithelial cells is a useful in vitro system to study normal biological processes and lung disease mechanisms, to develop new therapies, and to assess the biological perturbations induced by environmental pollutants. Herein, we investigate whether the perturbations induced by cigarette smoke (CS) and observed in the epithelium of smokers' airways are reproducible in this in vitro system (AIR-100 tissue), which has been shown to recapitulate most of the characteristics of the human bronchial epithelium. Human AIR-100 tissues were exposed to mainstream CS for 7, 14, 21, or 28 min at the air-liquid interface, and we investigated various biological endpoints [e.g., gene expression and microRNA profiles, matrix metalloproteinase 1 (MMP-1) release] at multiple postexposure time points (0.5, 2, 4, 24, 48 h). By performing a Gene Set Enrichment Analysis, we observed a significant enrichment of human smokers' bronchial epithelium gene signatures derived from different public transcriptomics datasets in CS-exposed AIR-100 tissue. Comparison of in vitro microRNA profiles with microRNA data from healthy smokers highlighted various highly translatable microRNAs associated with inflammation or with cell cycle processes that are known to be perturbed by CS in lung tissue. We also found a dose-dependent increase of MMP-1 release by AIR-100 tissue 48 h after CS exposure in agreement with the known effect of CS on this collagenase expression in smokers' tissues. In conclusion, a similar biological perturbation than the one observed in vivo in smokers' airway epithelium could be induced after a single CS exposure of a human organotypic bronchial epithelium-like tissue culture.
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Brônquios , Células Epiteliais , Metaloproteinase 1 da Matriz/biossíntese , Fumar , Poluição por Fumaça de Tabaco/efeitos adversos , Adulto , Brônquios/enzimologia , Brônquios/patologia , Células Cultivadas , Relação Dose-Resposta a Droga , Células Epiteliais/enzimologia , Células Epiteliais/patologia , Regulação Enzimológica da Expressão Gênica/efeitos dos fármacos , Humanos , Inflamação/induzido quimicamente , Inflamação/enzimologia , Inflamação/patologia , Masculino , MicroRNAs/metabolismo , Fumar/metabolismo , Fumar/patologia , Fatores de TempoRESUMO
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.
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Redes Reguladoras de Genes/genética , Mucosa Respiratória/fisiologia , Transcriptoma/genética , Animais , Ciclo Celular/efeitos dos fármacos , Ciclo Celular/genética , Simulação por Computador , Humanos , Camundongos , Camundongos Knockout , Poluição por Fumaça de Tabaco/efeitos adversosRESUMO
As one of the leading causes for dementia in the population, it is imperative that we discern exactly why Alzheimer's disease (AD) has a strong molecular association with beta-amyloid and tau. Although a clear understanding about etiology and pathogenesis of AD remains unsolved, scientists worldwide have dedicated significant efforts to discovering the molecular interactions linked to the pathological characteristics and potential treatments. Knowledge representations, such as domain ontologies encompassing our current understanding about AD, could greatly assist and contribute to disease research. This paper describes the construction and application of the integrated Alzheimer's Disease Ontology (ADO), combining selected concepts from the former version of the ADO and the Alzheimer's Disease Mapping Ontology (ADMO). In addition to the existing entities available from these knowledge models, essential knowledge about AD from public sources, such as newly discovered risk factor genes and novel treatments, was also integrated. The ADO can also be leveraged in text mining scenarios given that it is conceptually enriched with domain-specific knowledge as well as their relations. The integrated ADO consists of 39 855 total axioms. The ontology covers many aspects of the AD domain, including risk factor genes, clinical features, treatments and experimental models. The ontology complies with the Open Biological and Biomedical Ontology principles and was accepted by the foundry. In this paper, we illustrate the role of the presented ontology in extracting textual information from the SCAIView database and key measures in an ADO-based corpus. Database URL: https://academic.oup.com/database.
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Doença de Alzheimer , Ontologias Biológicas , Humanos , Doença de Alzheimer/genética , Mineração de DadosRESUMO
Motivation: Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. Results: The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28â151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. Availability and implementation: The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates.
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Clinical and medical knowledge evolve and this causes changes in concepts and terms that describe them. The objective of this work is to formally present an ontology-based standard architecture that will be used in the scenario of neurodegeneration research to maintain terminologies and their relations updated and coherent over the time. The proposed structure is composed by three elements that will allow the user to do a list of operations on the terminology resources explicitly contemplated by the Common Terminology Service Release 2 (CTS2).
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Projetos de Pesquisa , Terminologia como AssuntoRESUMO
Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple clinical trials investigating Gleevec's related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed.
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Polychlorinated biphenyls (PCBs) are a class of widespread environmental pollutants, commonly found in human blood, that have been suggested to be linked to the occurrence of sporadic Parkinson's disease (PD). It has been reported that some non-coplanar PCBs accumulate in the brains of female PD patients. To improve our understanding of the association between PCB exposure and PD risk we have applied whole transcriptome gene expression analysis in blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly, we observe that in females, blood levels of non-coplanar PCBs appear to be associated with expression levels of PD-specific genes. However, no such association was detected in males. Among the 131 PD-specific genes affected, 39 have been shown to display similar changes in expression levels in the substantia nigra of deceased PD patients. Especially among the down-regulated genes, transcripts of genes involved in neurotransmitter vesicle-related functions were predominant.
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Poluentes Ambientais/sangue , Expressão Gênica/efeitos dos fármacos , Doença de Parkinson/genética , Bifenilos Policlorados/sangue , Substância Negra/metabolismo , Adulto , Idoso , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Pessoa de Meia-Idade , Doença de Parkinson/sangue , Fatores SexuaisRESUMO
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
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Gene expression profiling in animal models exposed to cigarette mainstream smoke (CS) shapes up as a promising tool for investigating the molecular mechanisms involved in the onset and development of CS-related disease and may aid in the identification of disease candidate genes. Here we report on differential gene expression in lungs of rats exposed for 2, 7, and 13 weeks to 300 and 600 microg total particulate matter/l CS with sacrifice 2, 6, or 20 h after the last exposure. Regarding antioxidant and xenobiotic-metabolizing (phase I/II) enzymes, a stereotypic, mostly transient, expression pattern of differentially expressed genes was observed after each exposure period. The expression patterns were generally dose dependent for antioxidant and phase II genes and not dose dependent for phase I genes at the CS concentrations tested. However, with increasing length of exposure, there was a distinct, mostly sustained and dose-sensitive, expression of genes implicated in innate and adaptive immune responses, clearly pointing to an emerging inflammatory response. Notably, this inflammatory response included the expression of lung disease-related genes not yet linked to CS exposure, such as galectin-3, arginase 1, and chitinase, as well as genes encoding proteolytic enzymes. Finally, our experiments also revealed a CS exposure-dependent shift in the cyclical expression of genes involved in controlling the circadian rhythm. Altogether, these results provide further insight into the molecular mechanisms of CS-dependent disease onset and development and thus may also be useful for defining CS-specific molecular biomarkers of disease.
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Ritmo Circadiano , Inflamação/etiologia , Pulmão/metabolismo , Nicotiana/toxicidade , Fumaça/efeitos adversos , Transcrição Gênica , Fatores de Transcrição ARNTL , Animais , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Proteínas CLOCK , Expressão Gênica , Imunidade Inata , Masculino , Ratos , Ratos Sprague-Dawley , Transativadores/genética , Xenobióticos/metabolismoRESUMO
Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.
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Mineração de Dados , Doença , Pesquisa Translacional Biomédica , Humanos , Integração de SistemasRESUMO
Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.
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Cigarette smoke (CS) is a complex chemical mixture estimated to be composed of up to 5000 different chemicals, many of which are prooxidant. Here we show that, at least in vitro, the cellular response designed to combat oxidative stress resulting from CS exposure is primarily controlled by the transcription factor Nrf2, a principal inducer of antioxidant and phase II-related genes. The prominent role of Nrf2 in the cellular response to CS is substantiated by the following observations: In NIH3T3 cells exposed to aqueous extracts of CS (i) Nrf2 is strongly stabilized and becomes detectable in nuclear extracts. (ii) Nuclear localization of Nrf2 coincides with increased DNA binding of a putative Nrf2/MafK heterodimer to its cognate cis-regulatory site, i.e., the antioxidant-responsive element (ARE). (iii) Studies on the regulatory elements of the oxidative stress-inducible gene heme oxygenase-1 (hmox1) using various hmox1 promoter/luciferase reporter constructs revealed that the strong CS-dependent expression of this gene is primarily governed by the distal enhancers 1 ("E1") and 2 ("E2"), which both contain three canonical ARE-like stress-responsive elements (StREs). Notably, depletion of Nrf2 levels caused by RNA interference significantly compromised CS-induced hmox1 promoter activation, based on the distinct Nrf2 sensitivity exhibited by E1 and E2. Finally, (iv) siRNA-dependent knock-down of Nrf2 completely abrogated CS-induced expression of phase II-related genes. Taken together, these results confirm the outstanding role of Nrf2 both in sensing (oxidant) stress and in orchestrating an efficient transcriptional response aimed at resolving the stressing conditions.
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Heme Oxigenase-1/biossíntese , Proteínas de Membrana/biossíntese , Fator 2 Relacionado a NF-E2/metabolismo , Nicotiana/química , Fumaça/efeitos adversos , Animais , Glutamato-Cisteína Ligase/biossíntese , Camundongos , NAD(P)H Desidrogenase (Quinona) , NADPH Desidrogenase/biossíntese , Células NIH 3T3 , Regiões Promotoras Genéticas , Interferência de RNARESUMO
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".
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Chronic obstructive pulmonary disease (COPD) is a respiratory disorder caused by extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD develops are not fully understood, though it is believed that the disease process includes a genetic component, as not all smokers develop COPD. To investigate the mechanisms that lead to the development of COPD/emphysema, we measured whole genome gene expression and several COPD-relevant biological endpoints in mouse lung tissue after exposure to two CS doses for various lengths of time. A novel and powerful method, Reverse Engineering and Forward Simulation (REFS™), was employed to identify key molecular drivers by integrating the gene expression data and four measured COPD-relevant endpoints (matrix metalloproteinase (MMP) activity, MMP-9 levels, tissue inhibitor of metalloproteinase-1 levels and lung weight). An ensemble of molecular networks was generated using REFS™, and simulations showed that it could successfully recover the measured experimental data for gene expression and COPD-relevant endpoints. The ensemble of networks was then employed to simulate thousands of in silico gene knockdown experiments. Thirty-three molecular key drivers for the above four COPD-relevant endpoints were therefore identified, with the majority shown to be enriched in inflammation and COPD.