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Dimont, Emmanuel

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Dimont

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Emmanuel

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Dimont, Emmanuel
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    Methods for the Analysis of Differential Composition of Gene Expression
    (2015-01-21) Dimont, Emmanuel; Liu, Xiaole; Mar, Jessica
    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.
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    Urinary Benzene Biomarkers and DNA Methylation in Bulgarian Petrochemical Workers: Study Findings and Comparison of Linear and Beta Regression Models
    (Public Library of Science, 2012) Seow, Wei Jie; Pesatori, Angela Cecilia; Dimont, Emmanuel; Farmer, Peter B.; Albetti, Benedetta; Ettinger, Adrienne; Bollati, Valentina; Bolognesi, Claudia; Roggieri, Paola; Panev, Teodor I.; Georgieva, Tzveta; Merlo, Domenico Franco; Bertazzi, Pier Alberto; Baccarelli, Andrea
    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.