Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

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: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

A crowd-sourcing approach for the construction of species-specific cell signaling networks

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: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

Construction of a computable cell proliferation network focused on non-diseased lung cells

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

A crowd-sourcing approach for the construction of species-specific cell signaling networks

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: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Different Transcriptional Control of Metabolism and Extracellular Matrix in Visceral and Subcutaneous Fat of Obese and Rimonabant Treated Mice

BACKGROUND: 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. METHODOLOGY: 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. CONCLUSION: 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

Innate cell recognition of Lipopolysaccharide and other bacterial molecules

Une étape critique de la pathogenèse du sepsis est la reconnaissance des molécules bactériennes telles que les lipopolysaccharides (LPS) par les cellules de l'immunité innée. L'interaction du LPS, complexé à la "lipopolysaccharide binding protein" (LBP), avec les récepteurs CD14 et TLR4/MD-2 présents à la surface des cellules myéloïdes induit l'activation cellulaire. Parallèlement, afin d'être détoxifié, le LPS est internalisé de façon dépendante du CD14 par une voie de macropinocytose dans les macrophages. Le complexe récepteur TLR4/MD-2 est nécessaire pour transmettre le signal induit par le LPS au niveau intracytoplasmique. Durant la différenciation des cellules myéloïdes en macrophages ou en cellules dendritiques immatures et matures, l'expression de TLR4, MD-2, et CD14 est modulée et ceci semble conditionner la réponse des cellules au LPS. Par conséquent, la famille des TLRs et le MD-2 constituent de nouvelles cibles thérapeutiques pour le développement de traitements spécifiques pour la maladie du sepsis