Urinary Benzene Biomarkers and DNA Methylation in Bulgarian Petrochemical Workers: Study Findings and Comparison of Linear and Beta Regression Models

Chronic occupational exposure to benzene is associated with an increased risk of hematological malignancies such as acute myeloid leukemia (AML), but the underlying mechanisms are still unclear. The main objective of this study was to investigate the association between benzene exposure and DNA methylation, both in repeated elements and candidate genes, in a population of 158 Bulgarian petrochemical workers and 50 unexposed office workers. Exposure assessment included personal monitoring of airborne benzene at work and urinary biomarkers of benzene metabolism (S-phenylmercapturic acid [SPMA] and trans,trans-muconic acid [t,t-MA]) at the end of the work-shift. The median levels of airborne benzene, SPMA and t,t-MA in workers were 0.46 ppm, 15.5 µg/L and 711 µg/L respectively, and exposure levels were significantly lower in the controls. Repeated-element DNA methylation was measured in Alu and LINE-1, and gene-specific methylation in MAGE and p15. DNA methylation levels were not significantly different between exposed workers and controls (P>0.05). Both ordinary least squares (OLS) and beta-regression models were used to estimate benzene-methylation associations. Beta-regression showed better model specification, as reflected in improved coefficient of determination (pseudo $R^2$) and Akaike’s information criterion (AIC). In beta-regression, we found statistically significant reductions in LINE-1 (−0.15%, P<0.01) and p15 (−0.096%, P<0.01) mean methylation levels with each interquartile range (IQR) increase in SPMA. This study showed statistically significant but weak associations of LINE-1 and p15 hypomethylation with SPMA in Bulgarian petrochemical workers. We showed that beta-regression is more appropriate than OLS regression for fitting methylation data

Gateways to the FANTOM5 promoter level mammalian expression atlas

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0560-6) contains supplementary material, which is available to authorized users

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X's gene content, gene expression, and evolution

Methods for the Analysis of Differential Composition of Gene Expression

Modern next-generation sequencing and microarray-based assays have empowered the computational biologist to measure various aspects of biological activity. This has led to the growth of genomics, transcriptomics and proteomics as fields of study of the complete set of DNA, RNA and proteins in living cells respectively. One major challenge in the analysis of this data, however, has been the widespread lack of sufficiently large sample sizes due to the high cost of new emerging technologies, making statistical inference difficult. In addition, due to the hierarchical nature of the various types of data, it is important to correctly integrate them to make meaningful biological discoveries and better informed decisions for the successful treatment of disease. In this dissertation I propose: (1) a novel method for more powerful statistical testing of differential digital gene expression between two conditions, (2) a framework for the integration of multi-level biologic data, demonstrated with the compositional analysis of gene expression and its link to promoter structure, and (3) an extension to a more complex generalized linear modeling framework, demonstrated with the compositional analysis of gene expression and its link to pathway structure adjusted for confounding covariates

Association Estimates of Urinary Biomarkers, SPMA and t,t-MA, on Repeated-element and Gene-Specific Percent Methylation using OLS Regression.

<p>Abbreviations: SPMA, S-phenylmercapturic acid; t,t-MA, Trans-trans-muconic acid; AIC, Akaike’s information criterion.</p>*<p><i>P</i><0.05.</p>a<p>Model adjusted for age, sex, smoking history, education, ETS hours.</p>b<p>Coefficient refers to the change in methylation % per IQR change in exposure variable.</p>c<p>Adjusted R^2.</p

Characteristics of 158 Petrochemical Workers and 50 Controls, Bulgaria from 1999– 2000.

<p>Abbreviations: ETS, environmental tobacco smoke.</p>*<p><i>P</i><0.05.</p>a<p>Categorical variables are expressed as n (%), and continuous variables are expressed as mean (SD).</p>b<p>P-values were obtained from Pearson’s chi-square test for categorical variables and Welch two-sample t-test for continuous variables and Wilcoxon rank-sum test for non-normally distribution variables.</p

Association of S-phenylmercapturic acid (SPMA) with DNA methylation in Alu, LINE-1, <i>MAGE</i> and <i>p15</i> methylation.

<p>Fitted beta regression models of repeated-element and gene-specific methylation % versus log(SPMA), adjusted for potential confounders as described in the text. Lines correspond to fitted mean trajectories from beta regression models using the logit link, evaluated for hypothetical individuals with sample mean covariate values (petrochemical workers, non-smoking male, age: 40, ETS: 5.3 hours, education: middle-school). P-values shown correspond to main associations of SPMA in each model.</p

Association Estimates of Urinary Biomarkers, SPMA and t,t-MA, on Repeated-element and Gene-Specific Percent Methylation using Beta-regression.

<p>Abbreviations: SPMA, S-phenylmercapturic acid; t,t-MA, Trans-trans-muconic acid; AIC, Akaike’s information criterion.</p>*<p><i>P</i><0.05.</p>a<p>Model adjusted for age, sex, smoking history, education, ETS hours.</p>b<p>Coefficient refers to the change in methylation % per IQR change in exposure variable.</p>c<p>Pseudo-adjusted R^2.</p