Kati Illouz

Toxicogenomic Analysis of Gender, Chemical, and Dose Effects in Livers of TCDD- or Aroclor 1254–Exposed Rats Using a Multifactor Linear Model

Chronic exposure of Sprague-Dawley (SD) rats to either 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or Aroclor 1254 results in female-selective induction of hepatic tumors. The relative potency of dioxins and polychlorinated biphenyl mixtures, such as Aroclor 1254, is often estimated using the internationally endorsed toxic equivalency (TEQ) approach. Comparing the genome wide changes in gene expression in both genders following exposure to TEQ doses of these chemicals should identify critical sets of early response genes while further defining the concept of the TEQ of halogenated aromatic hydrocarbons. Aroclor 1254 at 0.6, 6.0, and 60 mg/kg body weight and TEQ doses of TCDD (0.3 and 3.0 mug/kg), calculated to match the top two Aroclor 1254 doses, were orally administered to SD rats for three consecutive days. Day 4 gene expression in hepatic tissue was determined using microarrays. A linear mixed-effects statistical model was developed to analyze the data in relation to treatment, gender, and gender * treatment (G*T) interactions. The genes most changed included 54 genes with and 51 genes without a significant model G*T term. The known aryl hydrocarbon receptor (AHR) battery genes (Cyp1a1, Cyp1a2, Cyp1b1, Aldh3a1), and novel genes, responded in a TEQ dose-dependent manner in both genders. However, an important observation was the apparent disruption of sexually dimorphic basal gene expression, particularly for female rats. Because many of these genes are involved in steroid metabolism, exposure to either TCDD or Aroclor 1254 could disrupt proliferative signals more in female rats as a possible consequence of altered estrogen metabolism. This study extends the findings of previous rodent bioassays by identifying groups of genes, other than the well-characterized AHR response genes, whose disruption may be important in the tumorigenic mechanism in this rat strain.

A Multi-Level Trend-Renewal Process for Modeling Systems With Recurrence Data

ABSTRACT A repairable system is a system that can be restored to an operational state after a repair event. The system may experience multiple events over time that are called recurrent events. To model the recurrent event data, the renewal process (RP), the nonhomogenous Poisson process (NHPP), and the trend-renewal process (TRP) are often used. Compared to the RP and NHPP, the TRP is more flexible for modeling, because it includes both RP and NHPP as special cases. However, for a multi-level system (e.g., system, subsystem, and component levels), the original TRP model may not be adequate if the repair is effected by a subsystem replacement and if subsystem-level replacement events affect the rate of occurrence of the component-level replacement events. In this article, we propose a general class of models to describe replacement events in a multi-level repairable system by extending the TRP model. We also develop procedures for parameter estimation and the prediction of future events based on historical data. The proposed model and method are validated by simulation studies and are illustrated by an industrial application. This article has online supplementary materials.