Search

Worldwide, lung cancer is the most prevalent and lethal malignant disease. In addition to avoidance of the most predominant risk factor, i.e., tobacco use, consumption of high amounts of vegetables and fruits could be an effective means of preventing lung cancer. However, the molecular mechanisms underlying lung cancer risk reduction by vegetables are not clear. In the present study, the effect of vegetables on gene expression changes in the lungs of female C57BI/6 mice was investigated using cDNA microarray technology. The mice were fed 1 of 8 diets for 2 wk: a control diet containing no vegetables (diet 1); a diet containing a vegetable mixture at 100 (diet 2, 10% dose), 200 (diet 3, 20% dose), or 400 (diet 4, 40% dose) g/kg; or a diet containing cauliflower at 70 (diet 5, 7% dose); carrots at 73 (diet 6, 7.3% dose); peas at 226 (diet 7, 22.6% dose); or onions at 31 (diet 8, 3.1% dose) g/kg. The vegetable mixture consisted of these 4 individual vegetables. After the mice were killed, the lungs were removed and total RNA was isolated from the lungs for expression analysis of 602 genes involved in pathways of (anti)-carcinogenesis. The results of this study suggest that individual vegetables have a higher potential of modulating genes (5 from the 8 modulated genes) in favor of lung cancer risk prevention, in comparison with the vegetable mixture (2 from the 7 modulated genes); the other gene modulations are expected to enhance lung cancer risk. The pathways involved were miscellaneous and included cell growth, apoptosis, biotransformation, and immune response. Furthermore, carrots were able to modulate most gene expressions, and most of these effects occurred in processes that favored lung cancer risk prevention. The current study provides more insight into the genetic mechanisms by which vegetables, in particular carrots, can prevent lung cancer risk. KEY WORDS: * vegetables * microarrays * gene expression * C57BI/6 mice * lung

There is abundant epidemiological evidence that vegetable consumption decreases colorectal cancer (CRC) risk. However, the molecular targets in the genome are mostly unknown. The present study investigated the effects of vegetable consumption on gene expression in the colon mucosa of female C57BI/6 mice using cDNA microarray technology. Mice were fed one of 8 diets: a control diet containing no vegetables (diet 1); a diet containing 100 g/kg (diet 2, 10% dose), 200 g/kg (diet 3, 20% dose), or 400 g/kg (diet 4, 40% dose) of a vegetable mixture; or a diet containing 70 g/kg of cauliflower (diet 5, 7% dose), 73 g/kg of carrots (diet 6, 7.3% dose), 226 g/kg of peas (diet 7, 22.6% dose); or 31 g/kg of onions (diet 8, 3.1% dose). The vegetable mixture used in diets 2 to 4 consisted of the 4 individual vegetables used in diets 5 to 8: cauliflower (30% wet wt), carrots (30% wet wt), peas (30% wet wt), and onions (10% wet wt). To assess gene expression changes, colonic mucosal cells were collected after the mice were killed. Total RNA was isolated and microarray technology was used to measure the expression levels of 602 genes simultaneously. For 39 genes, significant dose-dependent effects were found, although in general the relations were not linear. For 15 genes, the altered expression could indeed explain reduced cancer risk at various stages of CRC development. Eleven genes were modulated by the vegetable mixture as well as by one or more of the individual vegetables. For 7 of the genes, the modulation by the mixture was due to the effect of a particular vegetable. These genes are of particular interest because they were consistently affected and could be involved in the prevention of CRC by vegetable consumption. J. Nutr. 135: 1879-1888, 2005. KEY WORDS: * vegetables * microarrays * gene expression * C57BI/6 mice * colon

Abstract Background We hypothesized that in Flanders (Belgium), the prevalence of at-risk genotypes for genotoxic effects decreases with age due to morbidity and mortality resulting from chronic diseases. Rather than polymorphisms in single genes, the interaction of multiple genetic polymorphisms in low penetrance genes involved in genotoxic effects might be of relevance. Methods Genotyping was performed on 399 randomly selected adults (aged 50-65) and on 442 randomly selected adolescents. Based on their involvement in processes relevant to genotoxicity, 28 low penetrance polymorphisms affecting the phenotype in 19 genes were selected (xenobiotic metabolism, oxidative stress defense and DNA repair, respectively 13, 6 and 9 polymorphisms). Polymorphisms which, based on available literature, could not clearly be categorized a priori as leading to an 'increased risk' or a 'protective effect' were excluded. Results The mean number of risk alleles for all investigated polymorphisms was found to be lower in the 'elderly' (17.0 ± 2.9) than the 'adolescent' (17.6 ± 3.1) subpopulation (P = 0.002). These results were not affected by gender nor smoking. The prevalence of a high (> 17 = median) number of risk alleles was less frequent in the 'elderly' (40.6%) than the 'adolescent' (51.4%) subpopulation (P = 0.002). In particular for phase II enzymes, the mean number of risk alleles was lower in the 'elderly' (4.3 ± 1.6 ) than the 'adolescent' age group (4.8 ± 1.9) P < 0.001 and the prevalence of a high (> 4 = median) number of risk alleles was less frequent in the 'elderly' (41.3%) than the adolescent subpopulation (56.3%, P < 0.001). The prevalence of a high (> 8 = median) number of risk alleles for DNA repair enzyme-coding genes was lower in the 'elderly' (37,3%) than the 'adolescent' subpopulation (45.6%, P = 0.017). Conclusions These observations are consistent with the hypothesis that, in Flanders, the prevalence of at-risk alleles in genes involved in genotoxic effects decreases with age, suggesting that persons carrying a higher number of at risk alleles (especially in phase II xenobiotic-metabolizing or DNA repair genes) are at a higher risk of morbidity and mortality from chronic diseases. Our findings also suggest that, regarding risk of disease associated with low penetrance polymorphisms, multiple polymorphisms should be taken into account, rather than single ones.

Abstract Background In vitro cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied in vitro but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on in vitro systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the in vitro model system and model toxicant, respectively. Results The study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD. Conclusions Untargeted profiling of the polar and apolar metabolites of in vitro cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.

Abstract Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways.

Abstract Background DNA microarrays are widely used in gene expression analyses. To increase throughput and minimize costs without reducing gene expression data obtained, we investigated whether four mRNA samples can be analyzed simultaneously by applying four different fluorescent dyes. Results Following tests for cross-talk of fluorescence signals, Alexa 488, Alexa 594, Cyanine 3 and Cyanine 5 were selected for hybridizations. For self-hybridizations, a single RNA sample was labelled with all dyes and hybridized on commercial cDNA arrays or on in-house spotted oligonucleotide arrays. Correlation coefficients for all combinations of dyes were above 0.9 on the cDNA array. On the oligonucleotide array they were above 0.8, except combinations with Alexa 488, which were approximately 0.5. Standard deviation of expression differences for replicate spots were similar on the cDNA array for all dye combinations, but on the oligonucleotide array combinations with Alexa 488 showed a higher variation. Conclusion In conclusion, the four dyes can be used simultaneously for gene expression experiments on the tested cDNA array, but only three dyes can be used on the tested oligonucleotide array. This was confirmed by hybridizations of control with test samples, as all combinations returned similar numbers of differentially expressed genes with comparable effects on gene expression.

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen- specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88% correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances. [ABSTRACT FROM AUTHOR]