Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Effect of cessation after short term exposure to cigarette smoke

ABSTRACT: Modern 'omic' 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. For both in vitro systems with simple exposures and in vivo systems with complex exposures, the method was able to identify and provide relative quantitation for the precise mechanisms that were impacted. For example, when applied to data from mice exposed to cigarette smoke, the methodology correctly identified known mechanistic effects of the exposure, including increased pulmonary inflammation, necrosis, and proliferation. 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.

INSTRUMENT(S): Affymetrix GeneChip(R) HT Scanner

ORGANISM(S): Mus musculus

SUBMITTER: Sam Ansari

PROVIDER: E-MTAB-1313 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

Project description:To study the effects of scarification on gene expression in the skin we performed microarray analysis on intact skin (control) and skin collected 2 h after scarification. Comparison of microarray data between the two groups showed that 451 probe sets (1.0% of a total 45038 probe sets on the arrays) representing 338 known genes were upregulated ≥ 2.0 fold in scarified skin samples when compared to controls. Further analyses of these genes showed that many were associated with an inflammatory immune response. Others were associated with wound-repair, response to stress, apoptosis and transcriptional regulation. The induction of high levels of genes encoding the neutrophil chemoattractants Cxcl1 and Cxcl2, the G-CSF receptor Csf3r, the GM-CSF receptor Csf2rb, the calcium binding proteins S100a8 and a9 (S100a8 and S100a9, respectively) the cytokines Ilb and Il6, and Cd14, Mmp9 and Sell, amongst many others, suggested a significant influx of neutrophillic granulocytes into the skin after scarification. Indeed, the expression of Cxcl2 and Il1b were up-regulated 50 fold in scarified skin when compared to intact skin. C57BL/6 mice (6-12 weeks old), 2-3 per group used. Prior to scarification approximately 1 cm2 area of hair covering the site of scarification was trimmed using curved scissors and then removed completely with an electric razor. Twenty-four hours later a 23-gauge needle was used to create a 5 mm long abrasion in the epidermal layers of the skin at the scarification site. 2 h later, ear skin was collected into RNAlater (Ambion, location) and stored at -80 oC prior to analysis. The quality of RNA for microarray analysis was first determined on an Agilent BioAnalyzer 2100 (Agilent Technologies). RNA was reverse transcribed to cDNA, amplified and labelled using the Genechip 3’ IVT Express Labelling Kit (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s instructions. Samples were hybridized to HT_MG-430_PM Affymetrix chips (Affymetrix), stained and scanned by ARK Genomics (ARK Genomics, Edinburgh, UK).

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Effect of cessation after short term exposure to cigarette smoke

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