AIR100 SysBio Approach
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Affymetrix GeneChip Scanner 3000 7G
ORGANISM(S): Homo sapiens
SUBMITTER: Sam Ansari
PROVIDER: E-MTAB-874 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:We have performed a 28 days rat inhalation study according to the OECD TG 412 by exposing rats for 28 days to filtered air (sham), or to a low, medium, or high concentration of mainstream (MS) cigarette smoke (CS) from the Reference cigarette 3R4F in addition to MS CS from a prototypic modified risk tobacco product (pMRTP) at the same nicotine concentration as the high 3R4F group, i.e., 23 ug nicotine/l. Following CS inhalation, rats mainly develop irritative stress-related adaptive changes in the upper airways and signs of inflammation in the lung, a common condition preceding smoking-related lung diseases such as COPD and lung cancer. In the current study, the feasibility of investigating disease-relevant molecular perturbations at the sites of histopathological changes towards a Systems systems Biologybiology-based risk assessment was tested, using state-of-the-art technologies for sample preparation, analysis, and interpretation of gene expression arrays in combination with the classical toxicology end points.
2014-05-30 | E-MTAB-1842 | biostudies-arrayexpress
Project description: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 further develop this in vitro model as a standard human airway assay by comparing the biological response observed after cigarette smoke (CS) exposure in vitro and published data from human bronchial epithelium of smokers. To this end, we exposed differentiated normal human bronchial epithelial cells (AIR-100 tissue) to mainstream CS for 7, 14, 21, or 28 min at the air-liquid interface and investigated various biological endpoints (e.g., gene expression and microRNA profiles, MMP-1 release) at multiple post-exposure time points (0.5, 2, 4, 24, 48 hours).
2012-04-01 | E-MTAB-877 | biostudies-arrayexpress
Project description:The adhesion of monocytic cells to the M-^SdysfunctionalM-^T endothelium constitutes a critical step in the initiation of atherosclerotic plaque formation. Cigarette smoke (CS) has been shown to contribute to the monocyte-endothelial adhesion process. Yet, the complex mechanisms underlying this event remain to be discovered. In the systemic compartment, monocytes and endothelial cells are exposed primarily to soluble constituents originating from the inhaled CS and absorbed through the lung alveolar epithelium and the adjacent microvascular endothelium. Considering this rationale, we developed an in vitro adhesion assay, intending to mimic the in vivo situation, to deeply investigate the impact of CS on the adhesion of monocytic cell to the endothelium. Using a transcriptomics approach followed by confirmation experiments, we were able to identify a key mechanism by which aqueous extract of CS (smoke-bubbled Phosphate Buffered Saline, sbPBS) promotes the adhesion of monocytic Mono Mac 6 (MM6) cells to the human umbilical vein endothelial cells (HUVECs). While CS-derived constituents directly provoke a strong oxidative stress response in both cell types, the induced expression of E-selectin, VCAM-1 and ICAM-1 adhesion molecules, responsible for the binding of MM6 cells to HUVECs, occurs through a proinflammatory paracrine effect. We demonstrate that this effect is mainly driven by TNF? produced by MM6 cells exposed to sbPBS. In conclusion, the development of our in vitro adhesion assay, intending to mimic the in vivo conditions, enabled to show that the adhesion of monocytic cells to endothelial cells is promoted through an indirect effect of the sbPBS.
2014-04-24 | E-MTAB-1173 | biostudies-arrayexpress
Project description:Chronic Obstructive Pulmonary Disease (COPD) is a respiratory disorder that is the result of extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD evolves are not fully understood though it is believed that the disease process includes a genetic component since not all smokers develop COPD. To investigate the mechanism leading to the development of COPD/emphysema, we performed an experiment in which whole genome gene expression and several COPD-relevant biological endpoints (MMP-9, MMP activity, TIMP-1 and lung weight) were measured in lung tissue after exposure to two doses of CS for various periods of time. A novel and powerful method, known as reverse engineering and forward simulation (REFS(TM)), was employed to identify key molecular drivers by integrating gene expression data and 4 measured COPD-relevant endpoints. An ensemble of molecular networks was generated using REFS(TM). Simulations showed that this ensemble could successfully recover the measured experimental data for gene expression and measured COPD-relevant endpoints. This ensemble of networks was then further employed to simulate thousands of in silico gene knockdown experiments. Based on the in silico gene knockdown, thirty-three molecular key drivers for the above four COPD-relevant endpoints were identified, with the majority of them being enriched in inflammation, emphysema and COPD.
2014-05-14 | E-MTAB-1426 | biostudies-arrayexpress
Project description:Exposure to cigarette smoke is a leading cause of lung diseases including chronic obstructive pulmonary disease and cancer. Cigarette smoke is a complex aerosol containing over 6000 chemicals, and thus it is difficult to determine individual contributions to overall toxicity, and the molecular mechanisms by which smoke constituents exert their effects. We selected three well-known harmful and potentially harmful constituents (HPHCs) in tobacco smoke: acrolein, formaldehyde and catechol and established a High Content Screening (HCS) method using normal human bronchial epithelial cells, which are the first bronchial cells in contact with cigarette smoke. The impact of each HPHC was investigated using 13 multi-parametric indicators of cellular toxicity and complemented with a microarray-based whole transcriptome analysis followed by a computational approach leveraging mechanistic network models to identify and quantify perturbed molecular pathways. HPHCs were evaluated over a wide range of concentrations and at different exposure time points (4 h, 8 h, and 24 h). By High Content Screening, the toxic effects of the three HPHCs could only be observed at the highest doses. Whole genome transcriptomics unraveled toxicity mechanisms at lower doses and earlier time points. The most prevalent toxicity mechanisms observed were: DNA damage/growth arrest, oxidative stress, mitochondrial stress and apoptosis/necrosis. In summary, combination of multiple toxicological endpoints with a systems-based impact assessment allows for a more robust scientific basis for the toxicological assessment of HPHCs that allows insight into time- and dose-dependent molecular perturbations of specific biological pathways. This approach allowed us to establish an in vitro Systems Toxicology platform that can be applied to a broader selection of HPHCs and their mixtures and can serve more generally as the basis for testing the impact of other environmental toxicants on normal bronchial epithelial cells.
2014-05-09 | E-MTAB-2080 | biostudies-arrayexpress
Project description:Modern M-^QomicsM-^R technologies, where changes in the expression of thousands of molecular species can be investigated in even a simple experiment, provide contemporary biomedical researchers with an unprecedented level of molecular granularity. An outstanding challenge is transformation of this experimental detail and complexity into meaningful biological insight. This is especially true for drug development and toxicological risk assessment, where molecular profiling data is increasingly being used to investigate the biological effects of a variety of exposures, from drugs to environmental factors, on human systems. Recently, we proposed a systems-based strategy for objectively predicting the mechanistic impact of biologically active substances from transcriptomic data, and report here on an initial implementation of this strategy. We applied a set of biological network models and novel scoring algorithms to investigate transcriptomic data from a range of experimental systems. Importantly, this approach enabled continuity between investigation at the molecular, pathway, and systems levels. When applied to targeted perturbations on in vitro systems, the method was able to identify and provide relative quantitation for the precise mechanisms that were impacted. In normal human bronchial epithelial (NHBE) cells, the method correctly indicated the temporal activation of the cell cycle following removal of a small molecule cyclin-dependent kinase inhibitor. In rat nasal epithelium exposed to formaldehyde, the method identified known mechanistic effects caused by exposure, including increased cell proliferation and necrosis. Importantly, many of the effects indicated by the methodology were supported by experimental endpoint data, providing further objective validation of the general approach. We propose that various fields of human disease research, from drug development to consumer product testing, could benefit from using this or similar approaches to evaluate the biological impact of exposures.
2013-04-01 | E-MTAB-1272 | biostudies-arrayexpress
Project description:Chronic obstructive pulmonary disease (COPD) is a complex pulmonary disorder primarily induced by cigarette smoking, and characterized by persistent airflow limitation. The mouse represents an important model for studying COPD pathologies such as lung emphysema. In this respect, a number of mechanistic studies have been performed, however the approaches were mostly focused on single gene analysis or characterization of cellular, inflammatory or histopathological changes without attempting a more comprehensive interpretation. In the present study we aimed at applying systems biology approach to identify genome-wide molecular mechanisms indicative of cigarette smoke (CS)-induced lung emphysema. The lung transcriptomes of five mouse models (C57BL/6, ApoE-/-, A/J, CD1, and Nrf2-/-), that are known to be susceptible to CS-induced emphysema development, were analyzed following prolonged (5-6 months) CS exposure. The investigation resulted in the confirmation of many existing mechanistic explanations underlying smoke-induced lung emphysema, including increased transcriptional activity of NF-?B, and increased levels of TNF-a, IFN-g, and IL-1b. More importantly, we predicted mechanisms without currently well-documented roles, including increased transcriptional activity of PU.1, STAT1, C/EBP, FOXM1, YY1 and N-cor, and increased IL-17 cytokine expression, and reduced protein expression of ITGB6 and CFTR. We also corroborated, by using targeted proteomic approaches, several predictions such as reduced expression of ITGB6 and increased expression of BRCA1, C/EBPs, PU.1, TNF-a, IL-1b or CSF2. We believe this study will provide more insights into better understanding of CS-induced molecular processes underlying emphysema development in mice that may eventually be relevant in humans.
2014-11-28 | E-MTAB-2756 | biostudies-arrayexpress
Project description:Inferring in humans biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine 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 M-^StranslateM-^T the results to humans. In this context, the Systems Biology Verification for Industrial Methodology for Process Verification in Research (SBV IMPROVER) initiative had run a Species Translation Challenge for the scientific community to explore and understand the limit of translatability from rodent to human using systems biology. Therefore, a multi-layer omics dataset was generated that 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, the generation, processing and quality control analysis of the multi-layer omics dataset. The datasets are accessible in public repositories could be leveraged for further translation studies.
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Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.<br>Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.<br>The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-04-15 | E-MTAB-1027 | biostudies-arrayexpress
Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-12-15 | E-MTAB-1311 | biostudies-arrayexpress