A role for gene variants in regulating the pharmacokinetics of systemically available toxicants has not yet been established. A panel of 18 genetically-diverse inbred mouse strains was used to determine the range of total exposure kinetic parameters in blood and bone marrow following a single oral administration of benzene (100 μg/kg) to male and female mice. Large ranges in several pharmacokinetic parameters were found when data from blood and bone marrow were analyzed. AUC and CL_F pharmacokinetic parameters in blood and bone marrow pharmacokinetics were strikingly different as were these parameters in males and females. Final clearance (CL_F) was found to be the most statistically robust pharmacokinetic parameter as it accounted for exposure of the matrix (AUC) and normalized for dose variations among the strains. The CL_F values in blood and bone marrow used for haplotype association mapping showed 331 and 164 quantitative trait loci with statistical significance, respectively (male mice; -logP>4). Two loci were found to be shared between males and females QTL bone marrow data sets and one common locus was found for male blood and bone marrow data. No overlap was found among blood QTL in males and females (or between blood and bone marrow data from females). Protein and mRNA expression data for the primary benzene-metabolizing enzymes CYP2E1 and UGT1A6 showed very little strain-dependent variation. Strain dependent differences in mRNA levels of NQO1 and MPO were small but statistically significant, as were those for GAPDH and β2-microglobulin. These data demonstrated that polymorphisms with the greatest contribution toward overall variations in systemic exposures occurred in genes encoding for non-metabolic proteins. While exposure does not equate to toxicity, identification of the genes regulating distribution and clearance may be useful for investigating host susceptibility to toxic effects following benzene exposure. This research was supported in part by the NIEHS NTP Grant N01ES45529, NIEHS Toxicology and Toxicogenomics Training Grant (5T32ES007091-29), NIEHS/NTP Division of Intramural Research, and Southwest Environmental Science Center Grant P3ES06694.

<p>Genetic variation is a natural occurrence in the genome that contributes to the phenotypic differences observed between individuals as well as the phenotypic outcomes of various diseases, including infectious disease and cancer. Single nucleotide polymorphisms (SNPs) have been identified as genetic factors influencing host susceptibility to infectious disease while the study of copy number variation (CNV) in various cancers has led to the identification of causal genetic factors influencing tumor formation and severity. In this work, we evaluated the association between genomic variation and disease phenotypes to identify SNPs contributing to host susceptibility in Staphylococcus aureus (<italic>S. aureus</italic>) infection and to characterize a nervous system brain tumor, known as oligodendroglioma (OD), using the CNV observed in tumors with varying degree of malignancy.</p><p>Using SNP data, we utilized a computational approach, known as in silico haplotype mapping (ISHM), to identify genomic regions significantly associated with susceptibility to <italic>S. aureus</italic> infection in inbred mouse strains. We conducted ISHM on four phenotypes collected from <italic>S. aureus</italic> infected mice and identified genes contained in the significant regions, which were considered to be potential candidate genes. Gene expression studies were then conducted on inbred mice considered to be resistant or susceptible to <italic>S. aureus</italic> infection to identify genes differentially expressed between the two groups, which provided biological validation of the genes identified in significant ISHM regions. Genes identified by both analyses were considered our top priority genes and known biological information about the genes was used to determine their function roles in susceptibility to <italic>S. aureus</italic> infection.</p><p> We then evaluated CNV in subtypes of ODs to characterize the tumors by their genomic aberrations. We conducted array-based comparative genomic hybridization (CGH) on 74 ODs to generate genomic profiles that were classified by tumor grade, providing insight about the genomic changes that typically occur in patients with OD ranging from the less to more severe tumor types. Additionally, smaller genomic intervals with substantial copy number differences between normal and OD DNA samples, known as minimal critical regions (MCRs), were identified among the tumors. The genomic regions with copy number changes were interrogated for genes and assessed for their biological roles in the tumors' phenotype and formation. This information was used to assess the validity of using genomic variation in these tumors to further classify these tumors in addition to standard classification techniques. </p><p> The studies described in this project demonstrate the utility of using genetic variation to study disease phenotypes and applying computational and experimental techniques to identify the underlying genetic factors contributing to disease pathogenesis. Moreover, the continued development of similar approaches could aid in the development of new diagnostic procedures as well as novel therapeutics for the generation of more personalized treatments. The genomic regions with copy number changes were interrogated for genes and assessed for their biological roles in the tumors' phenotype and formation. This information was used to assess the validity of using genomic variation in these tumors to further classify these tumors in addition to standard classification techniques.</p><p> The studies described in this project demonstrate the utility of using genetic variation to study disease phenotypes and applying computational and experimental techniques to identify the underlying genetic factors contributing to disease pathogenesis. Moreover, the continued development of similar approaches could aid in the development of new diagnostic procedures as well as novel therapeutics for the generation of more personalized treatments.</p> / Dissertation

No description available.