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1.  Transcriptional profiling and targeted proteomics reveals common molecular changes associated with cigarette smoke-induced lung emphysema development in five susceptible mouse strains 
Inflammation Research  2015;64(7):471-486.
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).
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.
The lung transcriptomes of five mouse models (C57BL/6, ApoE−/−, A/J, CD1, and Nrf2−/−) were analyzed following 5–7 months of CS exposure.
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.
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.
Electronic supplementary material
The online version of this article (doi:10.1007/s00011-015-0820-2) contains supplementary material, which is available to authorized users.
PMCID: PMC4464601  PMID: 25962837
Emphysema; Cigarette smoke; Systems biology; Mouse models; Gene expression; Molecular mechanisms
2.  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”.
PMCID: PMC4357630  PMID: 25788831
air liquid interface; cigarette smoke; mechanistic investigations; organotypic culture; systems biology
3.  A crowd-sourcing approach for the construction of species-specific cell signaling networks 
Bioinformatics  2014;31(4):484-491.
Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.
Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4325542  PMID: 25294919
4.  Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge 
Bioinformatics  2014;31(4):471-483.
Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating’ those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species.
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4325540  PMID: 25236459
5.  A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability 
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.
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.
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.
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.
PMCID: PMC4227037  PMID: 24965703
Vascular systems biology; Plaque destabilization; Vascular biology networks; Computational modeling; Atherosclerosis modeling
6.  The species translation challenge—A systems biology perspective on human and rat bronchial epithelial cells 
Scientific Data  2014;1:140009.
The biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and in the field of toxicology, and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to ‘translate’ the results to humans. In this context, the SBV IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research) collaborative initiative, which uses crowd-sourcing techniques to address fundamental questions in systems biology, invited scientists to deploy their own computational methodologies to make predictions on species translatability. A multi-layer systems biology dataset was generated that was comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, generation, processing and quality control analysis of the multi-layer omics dataset accessible in public repositories for further intra- and inter-species translation studies.
PMCID: PMC4322580  PMID: 25977767
7.  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.
PMCID: PMC3798292  PMID: 24151423
community curation; biological network models; reputation system; Biological Expression Language
8.  Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data 
BMC Genomics  2013;14:514.
High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as ” contrast data”) in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.).
To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset.
Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from
PMCID: PMC3750322  PMID: 23895370
Gene expression; Contrast data; Gene set; Gene set enrichment; Omics; Microarray; Next-generation sequencing; Reproducible research system; Knowledge acquisition
9.  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.
PMCID: PMC3733638  PMID: 23926424
cell proliferation; biological network model; reverse causal reasoning
10.  Verification of systems biology research in the age of collaborative competition 
BMC Proceedings  2012;6(Suppl 6):P48.
PMCID: PMC3467476
12.  A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue 
BMC Systems Biology  2011;5:168.
Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate.
We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung.
The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.
PMCID: PMC3224482  PMID: 22011616
13.  Construction of a computable cell proliferation network focused on non-diseased lung cells 
BMC Systems Biology  2011;5:105.
Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.
To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.
To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.
PMCID: PMC3160372  PMID: 21722388
14.  Glucagon-Like Peptide-1 Protects β-Cells Against Apoptosis by Increasing the Activity of an Igf-2/Igf-1 Receptor Autocrine Loop 
Diabetes  2009;58(8):1816-1825.
The gluco-incretin hormones glucagon-like peptide (GLP)-1 and gastric inhibitory peptide (GIP) protect β-cells against cytokine-induced apoptosis. Their action is initiated by binding to specific receptors that activate the cAMP signaling pathway, but the downstream events are not fully elucidated. Here we searched for mechanisms that may underlie this protective effect.
We performed comparative transcriptomic analysis of islets from control and GipR−/−;Glp-1-R−/− mice, which have increased sensitivity to cytokine-induced apoptosis. We found that IGF-1 receptor expression was markedly reduced in the mutant islets. Because the IGF-1 receptor signaling pathway is known for its antiapoptotic effect, we explored the relationship between gluco-incretin action, IGF-1 receptor expression and signaling, and apoptosis.
We found that GLP-1 robustly stimulated IGF-1 receptor expression and Akt phosphorylation and that increased Akt phosphorylation was dependent on IGF-1 but not insulin receptor expression. We demonstrated that GLP-1–induced Akt phosphorylation required active secretion, indicating the presence of an autocrine activation mechanism; we showed that activation of IGF-1 receptor signaling was dependent on the secretion of IGF-2. We demonstrated, both in MIN6 cell line and primary β-cells, that reducing IGF-1 receptor or IGF-2 expression or neutralizing secreted IGF-2 suppressed GLP-1–induced protection against apoptosis.
An IGF-2/IGF-1 receptor autocrine loop operates in β-cells. GLP-1 increases its activity by augmenting IGF-1 receptor expression and by stimulating secretion; this mechanism is required for GLP-1–induced protection against apoptosis. These findings may lead to novel ways of preventing β-cell loss in the pathogenesis of diabetes.
PMCID: PMC2712796  PMID: 19401425
15.  Different Transcriptional Control of Metabolism and Extracellular Matrix in Visceral and Subcutaneous Fat of Obese and Rimonabant Treated Mice 
PLoS ONE  2008;3(10):e3385.
The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined.
C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant.
Principal Findings
In VAT, “metabolic” genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas “structure” genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified “metabolic” and “structure” genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT.
VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.
PMCID: PMC2586343  PMID: 19030233

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