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Many new tobacco and related products (nTRP) have emerged on the market, with unknown health risks. Here, we present a conceptual model containing the factors and relations between them that contribute to the nTRP's health effects. Factors that determine attractiveness, addictiveness and toxicity of nTRP were defined based on previous assessments, literature, and expert discussions. Our model will aid in identifying key risk factors contributing to increased risk of adverse health effects for a product in a qualitative manner. Additionally, it can gauge attractiveness for specific user groups, as a determinant for population prevalence of use. Our model can be used to identify aspects of nTRP that require attention for public information or product regulation. As an example, we applied this to JUUL, a popular e-cigarette in the US. Aspects of concern for JUUL are its attractive and discrete shape, user-friendly prefilled pods, flavors, high aerosol nicotine levels, and liquids containing nicotine salts instead of free-based nicotine. The addictiveness and especially attractiveness are sufficiently high to have a large potential impact on population health due to its contribution to use and hence exposure. Products and their use can change over time; therefore market research and monitoring are crucial., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Diet plays a decisive role in promoting or preventing colon cancer. However, the specific effects of some nutrients remain unclear. The capacity of fruit and vegetables to prevent cancer has been associated with their fiber and antioxidant composition. We investigated whether consumption of a lyophilized red grape pomace containing proanthocyanidin-rich dietary fiber (grape antioxidant dietary fiber, GADF) by female C57BL/6J mice would affect the serum metabolic profile or colon mucosa gene expression using NMR techniques and DNA microarray, respectively. The mice were randomly assigned to 2 groups that for 2 wk consumed a standard rodent diet and were gavaged with 100 mg/kg body weight GADF suspended in water or an equivalent volume of plain tap water (10 mL/kg body weight). The amount of fiber supplemented was calculated to equal the current recommended daily levels of fiber consumption for humans. The inclusion of dietary GADF induced alterations in the expression of tumor suppressor genes and proto-oncogenes as well as the modulation of genes from pathways, including lipid biosynthesis, energy metabolism, cell cycle, and apoptosis. Overexpression of enzymes pertaining to the xenobiotic detoxifying system and endogenous antioxidant cell defenses was also observed. In summary, the genetic and metabolic profiles induced by GADF were consistent with the preventive effects of fiber and polyphenols. On the basis of these observations, we propose that GADF may contribute to reducing the risk of colon cancer.

The Teplice area in the Czech Republic is a mining district where elevated levels of air pollution including airborne carcinogens, have been demonstrated, especially during winter time. This environmental exposure can impact human health; in particular children may be more vulnerable. To study the impact of air pollution in children at the transcriptional level, peripheral blood cells were subjected to whole genome response analysis, in order to identify significantly modulated biological pathways and processes as a result of exposure. Using genome-wide oligonucleotide microarrays, we investigated differential gene expression in children from the Teplice area (n=23) and compared them with children from the rural control area of Prachatice (n=24). In an additional approach, individual gene expressions were correlated with individual peripheral blood lymphocyte micronuclei frequencies, in order to evaluate the linkage of individual gene expressions with an established biomarker of effect that is representative for increased genotoxic risk. Children from the Teplice area showed a significantly higher average micronuclei frequency than Prachatice children (p=0.023). For considerable numbers of genes, the expression differed significantly between the children from the two areas. Amongst these genes, considerable numbers of genes were observed to correlate significantly with the frequencies of micronuclei. The main biological process that appeared significantly affected overall was nucleosome assembly. This suggests an effect of air pollution on the primary structural unit of the condensed DNA. In addition, several other pathways were modulated. Based on the results of this study, we suggest that transcriptomic analysis represents a promising biomarker for environmental carcinogenesis.

Prediction of the toxic properties of chemicals based on modulation of gene expression profiles in exposed cells or animals is one of the major applications of toxicogenomics. Previously, we demonstrated that by Pearson correlation analysis of gene expression profiles from treated HepG2 cells it is possible to correctly discriminate and predict genotoxic from non-genotoxic carcinogens. Since to date many different supervised clustering methods for discrimination and prediction tests are available, we investigated whether application of the methods provided by the Whitehead Institute and Stanford University improved our initial prediction. Four different supervised clustering methods were applied for this comparison, namely Pearson correlation analysis (Pearson), nearest shrunken centroids analysis (NSC), K-nearest neighbour analysis (KNN) and Weighted voting (WV). For each supervised clustering method, three different approaches were followed: (1) using all the data points for all treatments, (2) exclusion of the samples with marginally affected gene expression profiles and (3) filtering out the gene expression signals that were hardly altered. On the complete data set, NSC, KNN and WV outperformed the Pearson test, but on the reduced data sets no clear difference was observed. Exclusion of samples with marginally affected profiles improved the prediction by all methods. For the various prediction models, gene sets of different compositions were selected; in these 27 genes appeared three times or more. These 27 genes are involved in many different biological processes and molecular functions, such as apoptosis, cell cycle control, regulation of transcription, and transporter activity, many of them related to the carcinogenic process. One gene, BAX, was selected in all 10 models, while ZFP36 was selected in 9, and AHR, MT1E and TTR in 8. Summarising, this study demonstrates that several supervised clustering methods can be used to discriminate certain genotoxic from non-genotoxic carcinogens by gene expression profiling in vitro in HepG2 cells. None of the methods clearly outperforms the others.

Two general mechanisms are implicated in chemical carcinogenesis. The first involves direct damage to DNA, referred to as genotoxic (GTX), to which the cell responds by repair of the damages, arrest of the cell cycle or induction of apoptosis. The second is non-DNA damaging, non-genotoxic (NGTX), in which a wide variety of cellular processes may be involved. Therefore, it can be hypothesized that modulation of the underlying gene expression patterns is profoundly distinct between GTX and NGTX carcinogens, and thus that expression profiling is applicable for classification of chemical carcinogens as GTX or NGTX. We investigated this hypothesis by analysing modulation of gene expression profiles induced by 20 chemical carcinogens in HepG2 cells with application of cDNA microarrays that contain 597 toxicologically relevant genes. In total, 22 treatments were included, divided in two sets. The training set consisted of 16 treatments (nine genotoxins and seven non-genotoxins) and the validation set of six treatments (three and three). Class discrimination models based on Pearson correlation analyses for the 20 most discriminating genes were developed with data from the training set, where after the models were tested with all data. Using all data, the correctness for classification of the carcinogens from the training set was clearly better than that for the validation set, namely 81 and 33%, respectively. Exclusion of the treatments that had only marginal effects on the expression profiles, improved the discrimination for the training and validation sets to 92 and 100% correctness, respectively. Exclusion of the gene expression signals that were hardly altered also improved classification, namely to 94 and 80%. Therefore, our study proves the principle that gene expression profiling can discriminate carcinogens with major differences in their mode of actions, namely genotoxins versus non-genotoxins.