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In semi-arid regions, the annual floods of alluvial plains, wetlands and lakes provide essential services to millions of farmers, livestock holders and fishers. The scale, number and dispersion of these water bodies limit however their hydrological understanding, whose complexity is exacerbated by the mutual interactions between water and society. Rising availability of high spatial and temporal resolution remote sensing imagery provide novel opportunities to improve the understanding of the hydrology of multiple water bodies and of their ability to support local livelihoods. Multi-scalar interdisciplinary research combining remote sensing, in situ observations, hydrological modelling and farmer interviews was developed to investigate multiple socio-hydrosystems in the Senegal floodplain. Surface water monitoring with MNDWI on multi-spectral imagery (Landsat 5, 7, 8 and Sentinel-2A) is used to identify intra-annual and inter-annual variations in water availability. Validation against field monitoring (R2=0.88) and photointerpretation of drone imagery highlights the benefits of Sentinel's increased resolution to monitor floods of minor surface area and duration and improve the representation of hysteresis in floodplains. Integrated into forthcoming river basin modelling, these results can help simulate the influence of upstream changes on the hydrology and associated services of these complex socio-hydrosystems and improve watershed management.

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., (© 2024. The Author(s).)

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., (© 2023. The Author(s).)

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., Competing Interests: All authors are employees of Philip Morris International., (Copyright © 2022 Messinis, Poussin, Latino, Eb-Levadoux, Dulize, Peric, Guedj, Titz, Ivanov, Peitsch and Hoeng.)

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., (© 2022. The Author(s).)

Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality., (© 2021. The Author(s).)

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., (© 2021. The Author(s).)

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., Competing Interests: The work reported in this publication involved candidate/potential modified risk tobacco products developed by Philip Morris International. Philip Morris International is the sole source of funding and sponsor of this research. All authors are employees of PMI R&D or had worked for PMI R&D under contractual agreements., (© 2020 Published by Elsevier B.V.)

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.

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 Author(s) 2019. Published by Oxford University Press.)

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., (Copyright © 2018 PMI R&D, Philip Morris Products S.A. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 21 st 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.

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.

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., (Copyright © 2015 Z. Published by Elsevier Ireland Ltd.. All rights reserved.)

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., (© The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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/EBPβ, 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.

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".

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., (© The Author 2014. Published by Oxford University Press.)

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., (© The Author 2014. Published by Oxford University Press.)

The adhesion of monocytic cells to the "dysfunctional" endothelium constitutes a critical step in the initiation of atherosclerosis. Cigarette smoke (CS) has been shown to contribute to this process, the complex mechanism of which still needs to be unraveled. We developed an in vitro adhesion assay to investigate the CS-induced adhesion of monocytic MM6 cells to human umbilical vein endothelial cells (HUVECs) following exposure to an aqueous CS extract (smoke-bubbled phosphate buffered saline: sbPBS), reasoning that in vivo monocytes and endothelial cells are exposed primarily to soluble constituents from inhaled CS absorbed through the lung alveolar wall. MM6 cell adhesion was increased exclusively by the conditioned medium from sbPBS-exposed MM6 cells, not by direct sbPBS exposure of the HUVECs within a range of sbPBS doses. Using a transcriptomics approach followed by confirmation experiments, we identified different exposure effects on both cell types and a key mechanism by which sbPBS promoted the adhesion of MM6 cells to HUVECs. While sbPBS provoked a strong oxidative stress response in both cell types, the expression of E-selectin, VCAM-1 and ICAM-1, responsible for the adhesion of MM6 cells to HUVECs, was induced in the latter through a proinflammatory paracrine effect. We confirmed that this effect was driven mainly by TNFα produced by MM6 cells exposed to sbPBS. In conclusion, we have elucidated an indirect mechanism by which sbPBS increases the adhesion of monocytic cells to endothelial cells in this in vitro assay that was designed for tobacco product risk assessment while mimicking the in vivo exposure conditions as closely as possible., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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.

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.

Background: 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.)., Results: 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., Conclusion: 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 http://sourceforge.net/projects/confero/.

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.

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.

Background: 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., Results: 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., Conclusions: 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.

Objective: To identify metabolic pathways that may underlie susceptibility or resistance to high-fat diet-induced hepatic steatosis., Research Design and Methods: We performed comparative transcriptomic analysis of the livers of A/J and C57Bl/6 mice, which are, respectively, resistant and susceptible to high-fat diet-induced hepatosteatosis and obesity. Mice from both strains were fed a normal chow or a high-fat diet for 2, 10, and 30 days, and transcriptomic data were analyzed by time-dependent gene set enrichment analysis. Biochemical analysis of mitochondrial respiration was performed to confirm the transcriptomic analysis., Results: Time-dependent gene set enrichment analysis revealed a rapid, transient, and coordinate upregulation of 13 oxidative phosphorylation genes after initiation of high-fat diet feeding in the A/J, but not in the C57Bl/6, mouse livers. Biochemical analysis using liver mitochondria from both strains of mice confirmed a rapid increase by high-fat diet feeding of the respiration rate in A/J but not C57Bl/6 mice. Importantly, ATP production was the same in both types of mitochondria, indicating increased uncoupling of the A/J mitochondria., Conclusions: Together with previous data showing increased expression of mitochondrial β-oxidation genes in C57Bl/6 but not A/J mouse livers, our present study suggests that an important aspect of the adaptation of livers to high-fat diet feeding is to increase the activity of the oxidative phosphorylation chain and its uncoupling to dissipate the excess of incoming metabolic energy and to reduce the production of reactive oxygen species. The flexibility in oxidative phosphorylation activity may thus participate in the protection of A/J mouse livers against the initial damages induced by high-fat diet feeding that may lead to hepatosteatosis.

Background: 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., Results: 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., Conclusions: 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.

Accumulation of fat in the liver increases the risk to develop fibrosis and cirrhosis and is associated with development of the metabolic syndrome. Here, to identify genes or gene pathways that may underlie the genetic susceptibility to fat accumulation in liver, we studied A/J and C57Bl/6 mice that are resistant and sensitive to diet-induced hepatosteatosis and obesity, respectively. We performed comparative transcriptomic and lipidomic analysis of the livers of both strains of mice fed a high fat diet for 2, 10, and 30 days. We found that resistance to steatosis in A/J mice was associated with the following: (i) a coordinated up-regulation of 10 genes controlling peroxisome biogenesis and β-oxidation; (ii) an increased expression of the elongase Elovl5 and desaturases Fads1 and Fads2. In agreement with these observations, peroxisomal β-oxidation was increased in livers of A/J mice, and lipidomic analysis showed increased concentrations of long chain fatty acid-containing triglycerides, arachidonic acid-containing lysophosphatidylcholine, and 2-arachidonylglycerol, a cannabinoid receptor agonist. We found that the anti-inflammatory CB2 receptor was the main hepatic cannabinoid receptor, which was highly expressed in Kupffer cells. We further found that A/J mice had a lower pro-inflammatory state as determined by lower plasma levels and IL-1β and granulocyte-CSF and reduced hepatic expression of their mRNAs, which were found only in Kupffer cells. This suggests that increased 2-arachidonylglycerol production may limit Kupffer cell activity. Collectively, our data suggest that genetic variations in the expression of peroxisomal β-oxidation genes and of genes controlling the production of an anti-inflammatory lipid may underlie the differential susceptibility to diet-induced hepatic steatosis and pro-inflammatory state.

Isotope labeled tracers are commonly used to quantify the turnover rates of various metabolic intermediates and yield information regarding physiological regulation. Studies often only consider either one nutritional state (fasted or fed) and/or one question (e.g., measure of lipid or protein turnover). In this article, we consider a novel application combining the global approach of metabonomics with widespread stable isotope labeling as a way of being able to map metabolism in open mammalian systems, an approach we call "isotopomics". A total of 45 15-week-old male Zucker rats were administrated different amounts (from 0.5 to 8 mmol/kg) of sodium [1,2-(13)C(2)] acetate. Plasma samples taken at 1, 4, and 24 h were analyzed with (13)C nuclear magnetic resonance (NMR) and gas chromatography/mass spectrometry (GC/MS) to measure (13)C isotopic enrichment of 39 plasma metabolites across a wide range of compound classes (amino acids, short-chain fatty acids, lactate, glucose, and free fatty acids). Isotopic enrichment from 0.1-7.1 mole percent excess (MPE) for the highest dose could be reliably measured in 16 metabolites, and the kinetics of their (13)C isotopic enrichment are reported. Clustering metabolites based on (13)C kinetic curves enabled highlighting of time dependent patterns of (13)C distribution through the key metabolic pathways. These kinetic and quantitative data were reported into a biochemical map. This type of isotopomic approach for mapping dynamic metabolism in an open system has great potential for advancing our mechanistic knowledge of how different interventions and diseases can impact the metabolic response of animals and humans.

High-density lipoproteins (HDLs) protect pancreatic beta-cells against apoptosis. This property might relate to the increased risk to develop diabetes in patients with low HDL blood levels. However, the mechanisms by which HDLs protect beta-cells are poorly characterized. Here we used a transcriptomic approach to identify genes differentially modulated by HDLs in beta-cells subjected to apoptotic stimuli. The transcript encoding 4E-binding protein (4E-BP)1 was up-regulated by serum starvation, and HDLs blocked this increase. 4E-BP1 inhibits cap-dependent translation in its non- or hypophosphorylated state but it loses this ability when hyperphosphorylated. At the protein level, 4E-BP1 was also up-regulated in response to starvation and IL-1beta, and this was blunted by HDLs. Whereas an ectopic increase of 4E-BP1 expression induced beta-cell death, silencing 4E-BP1 increase with short hairpin RNAs inhibited the apoptotic-inducing capacities of starvation. HDLs can therefore protect beta-cells by blocking 4E-BP1 protein expression, but this is not the sole protective mechanism activated by HDLs. Indeed, HDLs blocked apoptosis induced by endoplasmic reticulum stress with no associated decrease in total 4E-BP1 induction. Although, HDLs favored the phosphorylation, and hence the inactivation of 4E-BP1 in these conditions, this appeared not to be required for HDL protection. Our results indicate that HDLs can protect beta-cells through modulation of 4E-BP1 depending on the type of stress stimuli.

Objective: The gluco-incretin hormones glucagon-like peptide (GLP)-1 and gastric inhibitory peptide (GIP) protect beta-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., Research Design and Methods: 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., Results: 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 beta-cells, that reducing IGF-1 receptor or IGF-2 expression or neutralizing secreted IGF-2 suppressed GLP-1-induced protection against apoptosis., Conclusions: An IGF-2/IGF-1 receptor autocrine loop operates in beta-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 beta-cell loss in the pathogenesis of diabetes.

Objective: Glucagon-like peptide-1 (GLP-1) is a growth and differentiation factor for mature beta-cells and their precursors. However, the overall effect of GLP-1 on increasing beta-cell mass in both in vivo and in vitro conditions is relatively small, and augmenting this effect would be beneficial for the treatment or prevention of type 1 and type 2 diabetes. Here, we searched for cellular mechanisms that may limit the proliferative effect of GLP-1 and tested whether blocking them could increase beta-cell proliferation., Research Design and Methods: We examined GLP-1-regulated genes in beta TC-Tet cells by cDNA microarrays. To assess the effect of some of these gene on cell proliferation, we reduced their expression using small heterogenous RNA in beta-cell lines and primary mouse islets and measured [(3)H]thymidine or 5'-bromo-2'-deoxyuridine incorporation., Results: We identified four negative regulators of intracellular signaling that were rapidly and strongly activated by GLP-1: the regulator of G-protein-signaling RGS2; the cAMP response element-binding protein (CREB) antagonists cAMP response element modulator (CREM)-alpha and ICERI; and the dual specificity phosphatase DUSP14, a negative regulator of the mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase 1/2 (ERK1/2) pathway. We show that knockdown of CREMalpha or DUSP14 or expression of a dominant-negative form of DUSP14 increased beta-cell line proliferation and enhanced the GLP-1-induced proliferation of primary beta-cells., Conclusions: Together, our data show that 1) the cAMP/protein kinase A/CREB and MAPK/ERK1/2 pathways can additively control beta-cell proliferation, 2) beta-cells have evolved several mechanisms limiting GLP-1-induced cellular proliferation, and 3) blocking these mechanisms increases the positive effect of GLP-1 on beta-cell mass.

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.

Genetically homogenous C57Bl/6 mice display differential metabolic adaptation when fed a high fat diet for 9 months. Most become obese and diabetic, but a significant fraction remains lean and diabetic or lean and non-diabetic. Here, we performed microarray analysis of "metabolic" transcripts expressed in liver and hindlimb muscles to evaluate: (i) whether expressed transcript patterns could indicate changes in metabolic pathways associated with the different phenotypes, (ii) how these changes differed from the early metabolic adaptation to short term high fat feeding, and (iii) whether gene classifiers could be established that were characteristic of each metabolic phenotype. Our data indicate that obesity/diabetes was associated with preserved hepatic lipogenic gene expression and increased plasma levels of very low density lipoprotein and, in muscle, with an increase in lipoprotein lipase gene expression. This suggests increased muscle fatty acid uptake, which may favor insulin resistance. In contrast, the lean mice showed a strong reduction in the expression of hepatic lipogenic genes, in particular of Scd-1, a gene linked to sensitivity to diet-induced obesity; the lean and non-diabetic mice presented an additional increased expression of eNos in liver. After 1 week of high fat feeding the liver gene expression pattern was distinct from that seen at 9 months in any of the three mouse groups, thus indicating progressive establishment of the different phenotypes. Strikingly, development of the obese phenotype involved re-expression of Scd-1 and other lipogenic genes. Finally, gene classifiers could be established that were characteristic of each metabolic phenotype. Together, these data suggest that epigenetic mechanisms influence gene expression patterns and metabolic fates.

Gram-negative bacterial endotoxin (a lipopolysaccharide (LPS)) specifically binds to CD14, a glycosylphosphatidyl inositol (GPI)-anchored surface myeloid glycoprotein. This interaction leads to cell activation, but it also promotes LPS internalization and detoxification. In this work, we investigated the route of LPS and CD14 internalization and the relevance of CD14 GPI anchor in the endocytic pathway. In promonocytic THP-1 cells transfected with a GPI or a chimeric integral form of CD14, we showed by differential buoyancy in sucrose density gradients that these two forms of CD14 were sorted to different plasma membrane subdomains. However, both forms of CD14 associated preferentially with the same surface microfilament-enriched microvilli or ruffles. Electron microscopic studies indicated that CD14 internalized via macropinocytosis, a process resembling that of phagocytosis, different from "classical" receptor-mediated endocytic pathways, such as clathrin-coated pits or caveolae. With cell warming, the CD14-enriched ruffles fused and formed large vesicles. Later, these vacuoles made stacks and condensed into phago-lysosomes. CD14 was specifically associated with all of these structures. Radiolabeled LPS internalization paralleled CD14 internalization. Confocal microscopic studies confirmed the co-localization of LPS and CD14 both at the cell surface and in endosomal compartments. The microfilament-disrupting, macropinocytosis blocking agent cytochalasin D inhibited LPS and CD14 internalization but did not prevent LPS-dependent activation, indicating that these two processes are dissociated.