We investigated the toxicity of benzo[a]pyrene (B[a]P), 1-nitropyrene (1-NP) and 3-nitrobenzanthrone (3-NBA) in A549 cells. Cells were treated for 4 h and 24 h with: B[a]P (0.1 and 1 μM), 1-NP (1 and 10 μM) and 3-NBA (0.5 and 5 μM). Bulky DNA adducts, lipid peroxidation, DNA and protein oxidation and mRNA expression of CYP1A1, CYP1B1, NQO1, POR, AKR1C2 and COX2 were analyzed. Bulky DNA adducts were induced after both treatment periods; the effect of 1-NP was weak. 3-NBA induced high levels of bulky DNA adducts even after 4-h treatment, suggesting rapid metabolic activation. Oxidative DNA damage was not affected. 1-NP caused protein oxidation and weak induction of lipid peroxidation after 4-h incubation. 3-NBA induced lipid peroxidation after 24-h treatment. Unlike B[a]P, induction of the aryl hydrocarbon receptor, measured as mRNA expression levels of CYP1A1 and CYP1B1, was low after treatment with polycyclic aromatic hydrocarbon (PAH) nitro-derivatives. All test compounds induced mRNA expression of NQO1, POR, and AKR1C2 after 24-h treatment. AKR1C2 expression indicates involvement of processes associated with reactive oxygen species generation. This was supported further by COX2 expression induced by 24-h treatment with 1-NP. In summary, 3-NBA was the most potent genotoxicant, whereas 1-NP exhibited the strongest oxidative properties.

• Keywords
• 1-nitropyrene, 3-nitrobenzanthrone, benzo[a]pyrene, bulky DNA adducts, gene expression, oxidative damage,
• MeSH
• DNA Adducts drug effects   genetics   MeSH
• Benz(a)Anthracenes toxicity   MeSH
• Benzo(a)pyrene toxicity   MeSH
• A549 Cells MeSH
• Cyclooxygenase 2 genetics   MeSH
• Cytochrome P-450 CYP1A1 genetics   MeSH
• Cytochrome P-450 CYP1B1 genetics   MeSH
• Hydroxysteroid Dehydrogenases genetics   MeSH
• Humans MeSH
• NAD(P)H Dehydrogenase (Quinone) genetics   MeSH
• Alveolar Epithelial Cells drug effects   metabolism   MeSH
• DNA Damage drug effects   genetics   MeSH
• Pyrenes toxicity   MeSH
• Cytochrome P-450 Enzyme System genetics   MeSH
• Vehicle Emissions toxicity   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• 1-nitropyrene MeSH Browser
• 3-nitrobenzanthrone MeSH Browser
• DNA Adducts MeSH
• AKR1C2 protein, human MeSH Browser
• Benz(a)Anthracenes MeSH
• Benzo(a)pyrene MeSH
• Cyclooxygenase 2 MeSH
• CYP1A1 protein, human MeSH Browser
• CYP1B1 protein, human MeSH Browser
• Cytochrome P-450 CYP1A1 MeSH
• Cytochrome P-450 CYP1B1 MeSH
• Hydroxysteroid Dehydrogenases MeSH
• NAD(P)H Dehydrogenase (Quinone) MeSH
• NQO1 protein, human MeSH Browser
• POR protein, human MeSH Browser
• PTGS2 protein, human MeSH Browser
• Pyrenes MeSH
• Cytochrome P-450 Enzyme System MeSH
• Vehicle Emissions MeSH

BACKGROUND: Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. METHODS: We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. RESULTS: The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. CONCLUSION: Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

• MeSH
• Gene Ontology MeSH
• Metabolic Networks and Pathways genetics   MeSH
• Operon genetics   MeSH
• Prokaryotic Cells metabolism   MeSH
• Gene Expression Regulation * MeSH
• Machine Learning MeSH
• Transcription Factors genetics   metabolism   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Transcription Factors MeSH