Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses.

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants' predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn's Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

Systems biology reveals anatabine to be an NRF2 activator.

Anatabine, an alkaloid present in plants of the So lanaceae family (including tobacco and eggplant), has been shown to ameliorate chronic inflammatory conditions in mouse models, such as Alzheimer's disease, Hashimoto's thyroiditis, multiple sclerosis, and intestinal inflammation. However, the mechanisms of action of anatabine remain unclear. To understand the impact of anatabine on cellular systems and identify the molecular pathways that are perturbed, we designed a study to examine the concentration-dependent effects of anatabine on various cell types by using a systems pharmacology approach. The resulting dataset, consisting of measurements of various omics data types at different time points, was analyzed by using multiple computational techniques. To identify concentration-dependent activated pathways, we performed linear modeling followed by gene set enrichment. To predict the functional partners of anatabine and the involved pathways, we harnessed the LINCS L1000 dataset's wealth of information and implemented integer linear programming on directed graphs, respectively. Finally, we experimentally verified our key computational predictions. Using an appropriate luciferase reporter cell system, we were able to demonstrate that anatabine treatment results in NRF2 (nuclear factor-erythroid factor 2-related factor 2) translocation, and our systematic phosphoproteomic assays showed that anatabine treatment results in activation of MAPK signaling. While there are certain areas to be explored in deciphering the exact anti-inflammatory mechanisms of action of anatabine and other NRF2 activators, we believe that anatabine constitutes an interesting molecule for its therapeutic potential in NRF2-related diseases.

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge.

BACKGROUND: Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard. RESULTS: Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species-which were not reliably predicted-helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample's Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall. CONCLUSIONS: kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Systems toxicology study reveals reduced impact of heated tobacco product aerosol extract relative to cigarette smoke on premature aging and exacerbation effects in aged aortic cells in vitro.

Aging and smoking are major risk factors for cardiovascular diseases (CVD). Our in vitro study compared, in the context of aging, the effects of the aerosol of Tobacco Heating System 2.2 (THS; an electrically heated tobacco product) and 3R4F reference cigarette smoke (CS) on processes that contribute to vascular pathomechanisms leading to CVD. Young and old human aortic smooth muscle cells (HAoSMC) were exposed to various concentrations of aqueous extracts (AE) from 3R4F CS [0.014-0.22 puffs/mL] or THS aerosol [0.11-1.76 puffs/mL] for 24 h. Key markers were measured by high-content imaging, transcriptomics profiling and multianalyte profiling. In our study, in vitro aging increased senescence, DNA damage, and inflammation and decreased proliferation in the HAoSMCs. At higher concentrations of 3R4F AE, young HAoSMCs behaved similarly to aged cells, while old HAoSMCs showed additional DNA damage and apoptosis effects. At 3R4F AE concentrations with the maximum effect, the THS AE showed no significant effect in young or old HAoSMCs. It required an approximately ten-fold higher concentration of THS AE to induce effects similar to those observed with 3R4F. These effects were independent of nicotine, which did not show a significant effect on HAoSMCs at any tested concentration. Our results show that 3R4F AE accelerates aging in young HAoSMCs and exacerbates the aging effect in old HAoSMCs in vitro, consistent with CS-related contributions to the risk of CVD. Relative to 3R4F AE, the THS AE showed a significantly reduced impact on HAoSMCs, suggesting its lower risk for vascular SMC-associated pathomechanisms leading to CVD.

Comparing the preclinical risk profile of inhalable candidate and potential candidate modified risk tobacco products: A bridging use case.

Cigarette smoking causes major preventable diseases, morbidity, and mortality worldwide. Smoking cessation and prevention of smoking initiation are the preferred means for reducing these risks. Less harmful tobacco products, termed modified-risk tobacco products (MRTP), are being developed as a potential alternative for current adult smokers who would otherwise continue smoking. According to a regulatory framework issued by the US Food and Drug Administration, a manufacturer must provide comprehensive scientific evidence that the product significantly reduces harm and the risk of tobacco-related diseases, in order to obtain marketing authorization for a new MRTP. For new tobacco products similar to an already approved predicate product, the FDA has foreseen a simplified procedure for assessing "substantial equivalence". In this article, we present a use case that bridges the nonclinical evidence from previous studies demonstrating the relatively reduced harm potential of two heat-not-burn products based on different tobacco heating principles. The nonclinical evidence was collected along a "causal chain of events leading to disease" (CELSD) to systematically follow the consequences of reduced exposure to toxicants (relative to cigarette smoke) through increasing levels of biological complexity up to disease manifestation in animal models of human disease. This approach leverages the principles of systems biology and toxicology as a basis for further extrapolation to human studies. The experimental results demonstrate a similarly reduced impact of both products on apical and molecular endpoints, no novel effects not seen with cigarette smoke exposure, and an effect of switching from cigarettes to either MRTP that is comparable to that of complete smoking cessation. Ideally, a subset of representative assays from the presented sequence along the CELSD could be sufficient for predicting similarity or substantial equivalence in the nonclinical impact of novel products; this would require further validation, for which the present use case could serve as a starting point.

3D human microvessel-on-a-chip model for studying monocyte-to-endothelium adhesion under flow - application in systems toxicology.

Lifestyle and genetic factors can lead to the development of atherosclerosis and, ultimately, cardiovascular adverse events. Rodent models are commonly used to investigate mechanism(s) of atherogenesis. However, the 3Rs principles, aiming to limit animal testing, encourage the scientific community to develop new physiologically relevant in vitro alternatives. Leveraging the 96-chip OrganoPlate®, a microfluidic platform, we have established a three-dimensional (3D) model of endothelial microvessels-on-a-chip under flow using primary human coronary arterial endothelial cells. As functional readout, we have set up an assay to measure the adhesion of monocytes to the lumen of perfused microvessels. For monitoring molecular changes in microvessels, we have established the staining and quantification of specific protein markers of inflammation and oxidative stress using high content imaging, as well as analyzed transcriptome changes using microarrays. To demonstrate its usefulness in systems toxicology, we leveraged our 3D vasculature-on-a-chip model to assess the impact of the Tobacco Heating System (THS) 2.2, a candidate modified risk tobacco product, and the 3R4F reference cigarette on the adhesion of monocytic cells to endothelial microvessels. Our results show that THS 2.2 aerosol-conditioned medium had a reduced effect on monocyte-endothelium adhesion compared with 3R4F smoke-conditioned medium. In conclusion, we have established a relevant 3D vasculature-on-a-chip model for investigating leukocyte-endothelial microvessel adhesion. A case study illustrates how the model can be used for product testing in the context of systems toxicology-based risk assessment. The current model and its potential further development options also open perspectives of applications in vascular disease research and drug discovery.

GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology.

SUMMARY: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the 'tcpl' core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate PDF reports for quality control and data processing. AVAILABILITY AND IMPLEMENTATION: GladiaTOX R package (bioconductor). Also available via: git clone https://github.com/philipmorrisintl/GladiaTOX.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status.

Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

In vitro systems toxicology-based assessment of the potential modified risk tobacco product CHTP 1.2 for vascular inflammation- and cytotoxicity-associated mechanisms promoting adhesion of monocytic cells to human coronary arterial endothelial cells.

Cigarette smoking causes cardiovascular diseases. Heating tobacco instead of burning it reduces the amount of toxic compounds in the aerosol and may exert a reduced impact on health compared with cigarette smoke. Aqueous extract from the aerosol of a potential modified risk tobacco product, the Carbon Heated Tobacco Product (CHTP) 1.2, was compared in vitro with aqueous extract from the smoke of a 3R4F reference cigarette for its impact on the adhesion of monocytic cells to artery endothelial cells. Human coronary artery endothelial cells (HCAEC) were treated for 4 h with conditioned media from human monocytic Mono Mac 6 (MM6) cells exposed to CHTP1.2 or 3R4F extracts for 2 h or directly with those extracts freshly generated. In vitro monocyte-endothelial cell adhesion was measured concomitantly with inflammatory, oxidative stress, cytotoxicity, and death markers. Furthermore, transcriptomics analyses enabled to quantify the level of perturbation in HCAECs, and provide biological interpretation for the underlying molecular changes following exposure to 3R4F or CHTP1.2 extract. Our systems toxicology study demonstrated that approximately 10-15-fold higher concentrations of the CHTP 1.2 aerosol extract were needed to elicit similar effects as the 3R4F smoke extract on cardiovascular disease-relevant inflammation and cytotoxicity-related mechanisms and markers investigated in vitro.

Interrogating the microbiome: experimental and computational considerations in support of study reproducibility.

The microbiome is an important factor in human health and disease and is investigated to develop novel therapeutics. Metagenomics leverages advances in sequencing technologies and computational analysis to identify and quantify the microorganisms present in a sample. This field has, however, not yet reached maturity and the international metagenomics community, aware of the current limitations and of the necessity for standardization, has started investigating sources of variability in experimental and computational workflows. The first studies have already resulted in the identification of crucial steps and factors affecting metagenomics data quality, quantification and interpretation. This review summarizes experimental and computational considerations for interrogating the microbiome and establishing reproducible and robust analysis workflows.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product.

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

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.

Systems Biology Reveals Cigarette Smoke-Induced Concentration-Dependent Direct and Indirect Mechanisms That Promote Monocyte-Endothelial Cell Adhesion.

Cigarette smoke (CS) affects the adhesion of monocytes to endothelial cells, a critical step in atherogenesis. Using an in vitro adhesion assay together with innovative computational systems biology approaches to analyze omics data, our study aimed at investigating CS-induced mechanisms by which monocyte-endothelial cell adhesion is promoted. Primary human coronary artery endothelial cells (HCAECs) were treated for 4 h with (1) conditioned media of human monocytic Mono Mac-6 (MM6) cells preincubated with low or high concentrations of aqueous CS extract (sbPBS) from reference cigarette 3R4F for 2 h (indirect treatment, I), (2) unconditioned media similarly prepared without MM6 cells (direct treatment, D), or (3) freshly generated sbPBS (fresh direct treatment, FD). sbPBS promoted MM6 cells-HCAECs adhesion following I and FD, but not D. In I, the effect was mediated at a low concentration through activation of vascular inflammation processes promoted in HCAECs by a paracrine effect of the soluble mediators secreted by sbPBS-treated MM6 cells. Tumor necrosis factor α (TNFα), a major inducer, was actually shed by unstable CS compound-activated TNFα-converting enzyme. In FD, the effect was triggered at a high concentration that also induced some toxicity. This effect was mediated through an yet unknown mechanism associated with a stress damage response promoted in HCAECs by unstable CS compounds present in freshly generated sbPBS, which had decayed in D unconditioned media. Aqueous CS extract directly and indirectly promotes monocytic cell-endothelial cell adhesion in vitro via distinct concentration-dependent mechanisms.

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

BACKGROUND: Mouse models are useful for studying cigarette smoke (CS)-induced chronic pulmonary pathologies such as lung emphysema. To enhance translation of large-scale omics data from mechanistic studies into pathophysiological changes, we have developed computational tools based on reverse causal reasoning (RCR). OBJECTIVE: In the present study we applied a systems biology approach leveraging RCR to identify molecular mechanistic explanations of pathophysiological changes associated with CS-induced lung emphysema in susceptible mice. METHODS: The lung transcriptomes of five mouse models (C57BL/6, ApoE (-/-) , A/J, CD1, and Nrf2 (-/-) ) were analyzed following 5-7 months of CS exposure. RESULTS: We predicted 39 molecular changes mostly related to inflammatory processes including known key emphysema drivers such as NF-κB and TLR4 signaling, and increased levels of TNF-α, CSF2, and several interleukins. More importantly, RCR predicted potential molecular mechanisms that are less well-established, including increased transcriptional activity of PU.1, STAT1, C/EBP, FOXM1, YY1, and N-COR, and reduced protein abundance of ITGB6 and CFTR. We corroborated several predictions using targeted proteomic approaches, demonstrating increased abundance of CSF2, C/EBPα, C/EBPß, PU.1, BRCA1, and STAT1. CONCLUSION: These systems biology-derived candidate mechanisms common to susceptible mouse models may enhance understanding of CS-induced molecular processes underlying emphysema development in mice and their relevancy for human chronic obstructive pulmonary disease.

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".

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: pmeyerr@us.ibm.com or Julia.Hoeng@pmi.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: ebilal@us.ibm.com or gustavo@us.ibm.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

BACKGROUND: Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of "omics" data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting. METHODS: We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis. RESULTS: Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques. CONCLUSIONS: We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability.