Prenatal environmental exposure and neurodevelopmentally important gene expression in malformed brain tissue from pediatric intractable epilepsy patients

The primary objective of this proposal was to determine whether mitochondrial oxidative stress and variation in a particular mtDNA lineage contribute to the risk of developing cortical dysplasia and are potential contributing factors in epileptogenesis in children. The occurrence of epilepsy in children is highly associated with malformations of cortical development (MCD). It appears that MCD might arise from developmental errors due to environmental exposures in combination with inherited variation in response to environmental exposures and mitochondrial function. Therefore, it is postulated that variation in a particular mtDNA lineage of children contributes to the effects of mitochondrial DNA damage on MCD phenotype. Quantitative PCR and dot blot were used to examine mitochondrial oxidative damage and single nucleotide polymorphism (SNP) in the mitochondrial genome in brain tissue from 48 pediatric intractable epilepsy patients from Miami Children’s Hospital and 11 control samples from NICHD Brain and Tissue Bank for Developmental Disorders. Epilepsy patients showed higher mtDNA copy number compared to normal health subjects (controls). Oxidative mtDNA damage was lower in non-neoplastic but higher in neoplastic epilepsy patients compared to controls. There was a trend of lower mtDNA oxidative damage in the non-neoplastic (MCD) patients compared to controls, yet, the reverse was observed in neoplastic (MCD and Non-MCD) epilepsy patients. The presence of mtDNA SNP and haplogroups did not show any statistically significant relationships with epilepsy phenotypes. However, SNPs G9804A and G9952A were found in higher frequencies in epilepsy samples. Logistic regression analysis showed no relationship between mtDNA oxidative stress, mtDNA copy number, mitochondrial haplogroups and SNP variations with epilepsy in pediatric patients. The levels of mtDNA copy number and oxidative mtDNA damage and the SNPs G9952A and T10010C predicted neoplastic epilepsy, however, this was not significant due to a small sample size of pediatric subjects. Findings of this study indicate that an increase in mtDNA content may be compensatory mechanisms for defective mitochondria in intractable epilepsy and brain tumor. Further validation of these findings related to mitochondrial genotypes and mitochondrial dysfunction in pediatric epilepsy and MCD may lay the ground for the development of new therapies and prevention strategies during embryogenesis.

Development of Gene Expression Markers of Acute Heat-Light Stress in Reef-Building Corals of the Genus Porites

Coral reefs are declining worldwide due to increased incidence of climate-induced coral bleaching, which will have widespread biodiversity and economic impacts. A simple method to measure the sub-bleaching level of heat-light stress experienced by corals would greatly inform reef management practices by making it possible to assess the distribution of bleaching risks among individual reef sites. Gene expression analysis based on quantitative PCR (qPCR) can be used as a diagnostic tool to determine coral condition in situ. We evaluated the expression of 13 candidate genes during heat-light stress in a common Caribbean coral Porites astreoides, and observed strong and consistent changes in gene expression in two independent experiments. Furthermore, we found that the apparent return to baseline expression levels during a recovery phase was rapid, despite visible signs of colony bleaching. We show that the response to acute heat-light stress in P. astreoides can be monitored by measuring the difference in expression of only two genes: Hsp16 and actin. We demonstrate that this assay discriminates between corals sampled from two field sites experiencing different temperatures. We also show that the assay is applicable to an Indo-Pacific congener, P. lobata, and therefore could potentially be used to diagnose acute heat-light stress on coral reefs worldwide.

An overview on Human Leukocyte Antigen (HLA) region gene expression during Pseudomonas aeruginosa infection

The human leukocyte antigen (HLA) complex is an extensively studied cluster of genes with immunoregulatory function. Pseudomonas aeruginosa is capable of infecting individuals with weakened immune systems, and is associated with a high mortality rate. Previous genetic studies of the HLA region have found correlations between bacterial infection and its effect on regulating HLA gene expressions to establish their infection. This project analyzes the expression of classical HLA loci (A, B, C, DR, DQ, DP) in human B cells and macrophage cells during the infection of virulent strains of P. aeruginosa. Cells were cultured and infected with different virulent live, and heat-killed strains of P. aeruginosa for different time periods. The mRNA was extracted and converted into cDNA followed by real-time quantitative PCR and data analysis. The Western Blot technique was used to identify the targeted protein’s cell surface expression. Infection with P. aeruginosa was found to inhibit the expression of HLA proteins. The PA14 strain inhibited expression of all targeted genes in all experiments. Infections with PA01 and PA103 showed different patterns depending on the incubation time and the targeted gene. These differences suggest that the three strains use various mechanisms to inhibit HLA protein expression.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.