Accelerated epigenetic aging as a risk factor for chronic obstructive pulmonary disease and decreased lung function in two prospective cohort studies.

Chronic obstructive pulmonary disease (COPD) is a frequent diagnosis in older individuals and contributor to global morbidity and mortality. Given the link between lung disease and aging, we need to understand how molecular indicators of aging relate to lung function and disease. Using data from the population-based KORA (Cooperative Health Research in the Region of Augsburg) surveys, we associated baseline epigenetic (DNA methylation) age acceleration with incident COPD and lung function. Models were adjusted for age, sex, smoking, height, weight, and baseline lung disease as appropriate. Associations were replicated in the Normative Aging Study. Of 770 KORA participants, 131 developed incident COPD over 7 years. Baseline accelerated epigenetic aging was significantly associated with incident COPD. The change in age acceleration (follow-up - baseline) was more strongly associated with COPD than baseline aging alone. The association between the change in age acceleration between baseline and follow-up and incident COPD replicated in the Normative Aging Study. Associations with spirometric lung function parameters were weaker than those with COPD, but a meta-analysis of both cohorts provide suggestive evidence of associations. Accelerated epigenetic aging, both baseline measures and changes over time, may be a risk factor for COPD and reduced lung function.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study.

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Computational model of the fathead minnow hypothalamic-pituitary-gonadal axis: Incorporating protein synthesis in improving predictability of responses to endocrine active chemicals.

There is international concern about chemicals that alter endocrine system function in humans and/or wildlife and subsequently cause adverse effects. We previously developed a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows exposed to a model aromatase inhibitor, fadrozole (FAD), to predict dose-response and time-course behaviors for apical reproductive endpoints. Initial efforts to develop a computational model describing adaptive responses to endocrine stress providing good fits to empirical plasma 17ß-estradiol (E2) data in exposed fish were only partially successful, which suggests that additional regulatory biology processes need to be considered. In this study, we addressed short-comings of the previous model by incorporating additional details concerning CYP19A (aromatase) protein synthesis. Predictions based on the revised model were evaluated using plasma E2 concentrations and ovarian cytochrome P450 (CYP) 19A aromatase mRNA data from two fathead minnow time-course experiments with FAD, as well as from a third 4-day study. The extended model provides better fits to measured E2 time-course concentrations, and the model accurately predicts CYP19A mRNA fold changes and plasma E2 dose-response from the 4-d concentration-response study. This study suggests that aromatase protein synthesis is an important process in the biological system to model the effects of FAD exposure.

Developing predictive approaches to characterize adaptive responses of the reproductive endocrine axis to aromatase inhibition: II. Computational modeling.

Endocrine-disrupting chemicals can affect reproduction and development in humans and wildlife. We developed a computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC) behaviors for endocrine effects of the aromatase inhibitor, fadrozole (FAD). The model describes adaptive responses to endocrine stress involving regulated secretion of a generic gonadotropin (LH/FSH) from the hypothalamic-pituitary complex. For model development, we used plasma 17ß-estradiol (E2) concentrations and ovarian cytochrome P450 (CYP) 19A aromatase mRNA data from two time-course experiments, each of which included both an exposure and a depuration phase, and plasma E2 data from a third 4-day study. Model parameters were estimated using E2 concentrations for 0, 0.5, and 3 µg/l FAD exposure concentrations, and good fits to these data were obtained. The model accurately predicted CYP19A mRNA fold changes for controls and three FAD doses (0, 0.5, and 3 µg/l) and plasma E2 dose response from the 4-day study. Comparing the model-predicted DRTC with experimental data provided insight into how the feedback control mechanisms in the HPG axis mediate these changes: specifically, adaptive changes in plasma E2 levels occurring during exposure and "overshoot" occurring postexposure. This study demonstrates the value of mechanistic modeling to examine and predict dynamic behaviors in perturbed systems. As this work progresses, we will obtain a refined understanding of how adaptive responses within the vertebrate HPG axis affect DRTC behaviors for aromatase inhibitors and other types of endocrine-active chemicals and apply that knowledge in support of risk assessments.

Developing predictive approaches to characterize adaptive responses of the reproductive endocrine axis to aromatase inhibition: I. Data generation in a small fish model.

Adaptive or compensatory responses to chemical exposure can significantly influence in vivo concentration-duration-response relationships. This study provided data to support development of a computational dynamic model of the hypothalamic-pituitary-gonadal axis of a model vertebrate and its response to aromatase inhibitors as a class of endocrine active chemicals. Fathead minnows (Pimephales promelas) were either exposed to the aromatase inhibitor fadrozole (0.5 or 30 µg/l) continuously for 1, 8, 12, 16, 20, 24, or 28 days or exposed for 8 days and then held in control water (no fadrozole) for an additional 4, 8, 12, 16, or 20 days. The time course of effects on ovarian steroid production, circulating 17ß-estradiol (E2) and vitellogenin (VTG) concentrations, and expression of steroidogenesis-related genes in the ovary was measured. Exposure to 30 µg fadrozole/l significantly reduced plasma E2 and VTG concentrations after just 1 day and those effects persisted throughout 28 days of exposure. In contrast, ex vivo E2 production was similar to that of controls on day 8-28 of exposure, whereas transcripts coding for aromatase and follicle-stimulating hormone receptor were elevated, suggesting a compensatory response. Following cessation of fadrozole exposure, ex vivo E2 and plasma E2 concentrations exceeded and then recovered to control levels, but plasma VTG concentrations did not, even after 20 days of depuration. Collectively these data provide several new insights into the nature and time course of adaptive responses to an aromatase inhibitor that support development of a computational model (see companion article).

Mechanistic computational model of steroidogenesis in H295R cells: role of oxysterols and cell proliferation to improve predictability of biochemical response to endocrine active chemical--metyrapone.

The human adrenocortical carcinoma cell line H295R is being used as an in vitro steroidogenesis screening assay to assess the impact of endocrine active chemicals (EACs) capable of altering steroid biosynthesis. To enhance the interpretation and quantitative application of measurement data in risk assessments, we are developing a mechanistic computational model of adrenal steroidogenesis in H295R cells to predict the synthesis of steroids from cholesterol (CHOL) and their biochemical response to EACs. We previously developed a deterministic model that describes the biosynthetic pathways for the conversion of CHOL to steroids and the kinetics for enzyme inhibition by the EAC, metyrapone (MET). In this study, we extended our dynamic model by (1) including a cell proliferation model supported by additional experiments and (2) adding a pathway for the biosynthesis of oxysterols (OXY), which are endogenous products of CHOL not linked to steroidogenesis. The cell proliferation model predictions closely matched the time-course measurements of the number of viable H295R cells. The extended steroidogenesis model estimates closely correspond to the measured time-course concentrations of CHOL and 14 adrenal steroids both in the cells and in the medium and the calculated time-course concentrations of OXY from control and MET-exposed cells. Our study demonstrates the improvement of the extended, more biologically realistic model to predict CHOL and steroid concentrations in H295R cells and medium and their dynamic biochemical response to the EAC, MET. This mechanistic modeling capability could help define mechanisms of action for poorly characterized chemicals for predictive risk assessments.

Computational model of steroidogenesis in human H295R cells to predict biochemical response to endocrine-active chemicals: model development for metyrapone.

BACKGROUND: An in vitro steroidogenesis assay using the human adrenocortical carcinoma cell line H295R is being evaluated as a possible screening assay to detect and assess the impact of endocrine-active chemicals (EACs) capable of altering steroid biosynthesis. Data interpretation and their quantitative use in human and ecological risk assessments can be enhanced with mechanistic computational models to help define mechanisms of action and improve understanding of intracellular concentration-response behavior. OBJECTIVES: The goal of this study was to develop a mechanistic computational model of the metabolic network of adrenal steroidogenesis to estimate the synthesis and secretion of adrenal steroids in human H295R cells and their biochemical response to steroidogenesis-disrupting EAC. METHODS: We developed a deterministic model that describes the biosynthetic pathways for the conversion of cholesterol to adrenal steroids and the kinetics for enzyme inhibition by metryrapone (MET), a model EAC. Using a nonlinear parameter estimation method, the model was fitted to the measurements from an in vitro steroidogenesis assay using H295R cells. RESULTS: Model-predicted steroid concentrations in cells and culture medium corresponded well to the time-course measurements from control and MET-exposed cells. A sensitivity analysis indicated the parameter uncertainties and identified transport and metabolic processes that most influenced the concentrations of primary adrenal steroids, aldosterone and cortisol. CONCLUSIONS: Our study demonstrates the feasibility of using a computational model of steroidogenesis to estimate steroid concentrations in vitro. This capability could be useful to help define mechanisms of action for poorly characterized chemicals and mixtures in support of predictive hazard and risk assessments with EACs.

Mechanistic computational model of ovarian steroidogenesis to predict biochemical responses to endocrine active compounds.

Sex steroids, which have an important role in a wide range of physiological and pathological processes, are synthesized primarily in the gonads and adrenal glands through a series of enzyme-mediated reactions. The activity of steroidogenic enzymes can be altered by a variety of endocrine active compounds (EAC), some of which are therapeutics and others that are environmental contaminants. A steady-state computational model of the intraovarian metabolic network was developed to predict the synthesis and secretion of testosterone (T) and estradiol (E2), and their responses to EAC. Model predictions were compared to data from an in vitro steroidogenesis assay with ovary explants from a small fish model, the fathead minnow. Model parameters were estimated using an iterative optimization algorithm. Model-predicted concentrations of T and E2 closely correspond to the time-course data from baseline (control) experiments, and dose-response data from experiments with the EAC, fadrozole (FAD). A sensitivity analysis of the model parameters identified specific transport and metabolic processes that most influence the concentrations of T and E2, which included uptake of cholesterol into the ovary, secretion of androstenedione (AD) from the ovary, and conversions of AD to T, and AD to estrone (E1). The sensitivity analysis also indicated the E1 pathway as the preferred pathway for E2 synthesis, as compared to the T pathway. Our study demonstrates the feasibility of using the steroidogenesis model to predict T and E2 concentrations, in vitro, while reducing model complexity with a steady-state assumption. This capability could be useful for pharmaceutical development and environmental health assessments with EAC.

MRI-guided thermal ablation therapy: model and parameter estimates to predict cell death from MR thermometry images.

Solid tumors and other pathologies can be treated using laser thermal ablation under interventional magnetic resonance imaging (iMRI) guidance. A model was developed to predict cell death from magnetic resonance (MR) thermometry measurements based on the temperature-time history, and validated using in vivo rabbit brain data. To align post-ablation T2-weighted spin-echo MR lesion images to gradient-echo MR images, from which temperature is derived, a registration method was used that aligned fiducials placed near the thermal lesion. The outer boundary of the hyperintense rim in the post-ablation MR lesion image was used as the boundary for cell death, as verified from histology. Model parameters were simultaneously estimated using an iterative optimization algorithm applied to every interesting voxel in 328 images from multiple experiments having various temperature histories. For a necrotic region of 766 voxels across all lesions, the model provided a voxel specificity and sensitivity of 98.1 and 78.5%, respectively. Mislabeled voxels were typically within one voxel from the segmented necrotic boundary with median distances of 0.77 and 0.22 mm for false positives (FP) and false negatives (FN), respectively. As compared to the critical temperature cell death model and the generalized Arrhenius model, our model typically predicted fewer FP and FN. This is good evidence that iMRI temperature maps can be used with our model to predict therapeutic regions in real-time during treatment.