Danitsja M. van Leeuwen

Genomic analysis suggests higher susceptibility of children to air pollution

Differences in biological responses to exposure to hazardous airborne substances between children and adults have been reported, suggesting children to be more susceptible. Aim of this study was to improve our understanding of differences in susceptibility in cancer risk associated with air pollution by comparing genome-wide gene expression profiles in peripheral blood of children and their parents. Gene expression analysis was performed in blood from children and parents living in two different regions in the Czech Republic with different levels of air pollution. Data were analyzed by two different approaches: one method first selected significantly differentially expressed genes and analyzed these gene lists for overrepresented biological processes, whereas the other applied the T-profiler tool to directly perform pathway analyses on the total gene set without preselection of significantly modulated gene expressions. In addition, gene expressions in both children and adults were investigated for associations with micronuclei frequencies. Both analysis approaches returned considerably more genes or gene groups and pathways that significantly differed between children from both regions than between parents. Very little overlap was observed between children and adults. The two most important biological processes or molecular functions significantly modulated in children, but not in adults, are nucleosome and immune response related. Our study suggests differences between children and adults in relation to air pollution exposure at the transcriptome level. The findings underline the necessity of implementing environmental health policy measures specifically for protecting childrens health.

Transcriptome Analysis in Peripheral Blood of Humans Exposed to Environmental Carcinogens: A Promising New Biomarker in Environmental Health Studies

Background Human carcinogenesis is known to be initiated and/or promoted by exposure to chemicals that occur in the environment. Molecular cancer epidemiology is used to identify human environmental cancer risks by applying a range of effect biomarkers, which tend to be nonspecific and do not generate insights into underlying modes of action. Toxicogenomic technologies may improve on this by providing the opportunity to identify molecular biomarkers consisting of altered gene expression profiles. Objectives The aim of the present study was to monitor the expression of selected genes in a random sample of adults in Flanders selected from specific regions with (presumably) different environmental burdens. Furthermore, associations of gene expression with blood and urinary measures of biomarkers of exposure, early phenotypic effects, and tumor markers were investigated. Results Individual gene expression of cytochrome p450 1B1, activating transcription factor 4, mitogen-activated protein kinase 14, superoxide dismutase 2 (Mn), chemokine (C-X-C motif) lig-and 1 (melanoma growth stimulating activity, alpha), diacylglycerol O-acyltransferase homolog 2 (mouse), tigger transposable element derived 3, and PTEN-induced putative kinase1 were measured by means of quantitative polymerase chain reaction in peripheral blood cells of 398 individuals. After correction for the confounding effect of tobacco smoking, inhabitants of the Olen region showed the highest differences in gene expression levels compared with inhabitants from the Gent and fruit cultivation regions. Importantly, we observed multiple significant correlations of particular gene expressions with blood and urinary measures of various environmental carcinogens. Conclusions Considering the observed significant differences between gene expression levels in inhabitants of various regions in Flanders and the associations of gene expression with blood or urinary measures of environmental carcinogens, we conclude that gene expression profiling appears promising as a tool for biological monitoring in relation to environmental exposures in humans.

Global Gene Expression Analysis in Cord Blood Reveals Gender-Specific Differences in Response to Carcinogenic Exposure In Utero

Background: It has been suggested that fetal carcinogenic exposure might lead to predisposition to develop cancer during childhood or in later life possibly through modulation of the fetal transcriptome. Because gender effects in the incidence of childhood cancers have been described, we hypothesized differences at the transcriptomic level in cord blood between male and female newborns as a consequence of fetal carcinogenic exposure. The objective was to investigate whether transcriptomic responses to dietary genotoxic and nongenotoxic carcinogens show gender-specific mechanisms-of-action relevant for chemical carcinogenesis. Methods: Global gene expression was applied in umbilical cord blood samples, the CALUX-assay was used for measuring dioxin(-like), androgen(-like), and estrogen(-like) internal exposure, and acrylamide–hemoglobin adduct levels were determined by mass spectrometry adduct-FIRE-procedureTM. To link gene expression to an established phenotypic biomarker of cancer risk, micronuclei frequencies were investigated. Results: While exposure levels did not differ between sexes at birth, important gender-specific differences were observed in gene expressions associated with these exposures linked with cell cycle, the immune system and more general cellular processes such as posttranslation. Moreover, oppositely correlating leukemia/lymphoma genes between male and female newborns were identified in relation to the different biomarkers of exposure that might be relevant to male-specific predisposition to develop these cancers in childhood. Conclusions/Impact: This study reveals different transcriptomic responses to environmental carcinogens between the sexes. In particular, male-specific TNF-alpha-NF-kB signaling upon dioxin exposure and activation of the Wnt-pathway in boys upon acrylamide exposure might represent possible mechanistic explanations for gender specificity in the incidence of childhood leukemia. Cancer Epidemiol Biomarkers Prev; 21(10); 1756–67. ©2012 AACR.

Transcriptomic Profile Indicative of Immunotoxic Exposure: In Vitro Studies in Peripheral Blood Mononuclear Cells

Investigating the immunotoxic effects of exposure to chemicals usually comprises evaluation of weight and histopathology of lymphoid tissues, various lymphocyte parameters in the circulation, and immune function. Immunotoxicity assessment is time consuming in humans or requires a high number of animals, making it expensive. Furthermore, reducing the use of animals in research is an important ethical and political issue. Immunotoxicogenomics represents a novel approach to investigate immunotoxicity able of overcoming these limitations. The current research, embedded in the European Union project NewGeneris, aimed to retrieve gene expression profiles that are indicative of exposure to immunotoxicants. To this end, whole-genome gene expression was investigated in human peripheral blood mononuclear cells in response to in vitro exposure to a range of immunotoxic chemicals (4-hydroxy-2-nonenal, aflatoxin B1, benzo[a]pyrene, deoxynivalenol, ethanol, malondialdehyde, polychlorinated biphenyl 153, and 2,3,7,8-tetrachlorodibenzo-p-dioxin) and nonimmunotoxic chemicals (acrylamide, dimethylnitrosamine, 2-amino-3-methyl-3H-imidazo[4,5-F]quinoline, and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine). Using Agilent oligonucleotide microarrays, whole-genome gene expression profiles were generated, which were analyzed using Genedatas Expressionist software. Using Recursive Feature Elimination and Support Vector Machine, a set of 48 genes was identified that distinguishes the immunotoxic from the nonimmunotoxic compounds. Analysis for enrichment of biological processes showed the gene set to be highly biologically and immunologically relevant. We conclude that we have identified a promising transcriptomic profile indicative of immunotoxic exposure.

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

BackgroundIn 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.ResultsIn 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.ConclusionCASh 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.

Bayesian Network Inference. Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.