Exposure to Polyfluoroalkyl Chemicals and Cholesterol, Body Weight, and Insulin Resistance in the General U.S. Population

BACKGROUND. Polyfluoroalkyl chemicals (PFCs) are used commonly in commercial applications and are detected in humans and the environment worldwide. Concern has been raised that they may disrupt lipid and weight regulation. OBJECTIVES. We investigated the relationship between PFC serum concentrations and lipid and weight outcomes in a large publicly available data set. METHODS. We analyzed data from the 2003-2004 National Health and Nutrition Examination Survey (NHANES) for participants 12-80 years of age. Using linear regression to control for covariates, we studied the association between serum concentrations of perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), and perfluorohexane sulfonic acid (PFHxS) and measures of cholesterol, body size, and insulin resistance. RESULTS. We observed a positive association between concentrations of PFOS, PFOA, and PFNA and total and non-high-density cholesterol. We found the opposite for PFHxS. Those in the highest quartile of PFOS exposure had total cholesterol levels 13.4 mg/dL [95% confidence interval (CI), 3.8-23.0] higher than those in the lowest quartile. For PFOA, PFNA, and PFHxS, effect estimates were 9.8 (95% CI, -0.2 to 19.7), 13.9 (95% CI, 1.9-25.9), and -7.0 (95% CI, -13.2 to -0.8), respectively. A similar pattern emerged when exposures were modeled continuously. We saw little evidence of a consistent association with body size or insulin resistance. CONCLUSIONS. This exploratory cross-sectional study is consistent with other epidemiologic studies in finding a positive association between PFOS and PFOA and cholesterol, despite much lower exposures in NHANES. Results for PFNA and PFHxS are novel, emphasizing the need to study PFCs other than PFOS and PFOA.National Institute of Environmental Health Sciences (R21ES013724, T32ES014562

Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor α agonists

BACKGROUND: Fibrates are a unique hypolipidemic drugs that lower plasma triglyceride and cholesterol levels through their action as peroxisome proliferator-activated receptor alpha (PPARα) agonists. The activation of PPARα leads to a cascade of events that result in the pharmacological (hypolipidemic) and adverse (carcinogenic) effects in rodent liver. RESULTS: To understand the molecular mechanisms responsible for the pleiotropic effects of PPARα agonists, we treated mouse primary hepatocytes with three PPARα agonists (bezafibrate, fenofibrate, and WY-14,643) at multiple concentrations (0, 10, 30, and 100 μM) for 24 hours. When primary hepatocytes were exposed to these agents, transactivation of PPARα was elevated as measured by luciferase assay. Global gene expression profiles in response to PPARα agonists were obtained by microarray analysis. Among differentially expressed genes (DEGs), there were 4, 8, and 21 genes commonly regulated by bezafibrate, fenofibrate, and WY-14,643 treatments across 3 doses, respectively, in a dose-dependent manner. Treatments with 100 μM of bezafibrate, fenofibrate, and WY-14,643 resulted in 151, 149, and 145 genes altered, respectively. Among them, 121 genes were commonly regulated by at least two drugs. Many genes are involved in fatty acid metabolism including oxidative reaction. Some of the gene changes were associated with production of reactive oxygen species, cell proliferation of peroxisomes, and hepatic disorders. In addition, 11 genes related to the development of liver cancer were observed. CONCLUSION: Our results suggest that treatment of PPARα agonists results in the production of oxidative stress and increased peroxisome proliferation, thus providing a better understanding of mechanisms underlying PPARα agonist-induced hepatic disorders and hepatocarcinomas

Effect of Synthetic Dietary Triglycerides: A Novel Research Paradigm for Nutrigenomics

The effect of dietary fats on human health and disease are likely mediated by changes in gene expression. Several transcription factors have been shown to respond to fatty acids, including SREBP-1c, NF-kappaB, RXRs, LXRs, FXR, HNF4alpha, and PPARs. However, it is unclear to what extent these transcription factors play a role in gene regulation by dietary fatty acids in vivo

Comparative Analysis of Gene Regulation by the Transcription Factor PPARα between Mouse and Human

Studies in mice have shown that PPARalpha is an important regulator of hepatic lipid metabolism and the acute phase response. However, little information is available on the role of PPARalpha in human liver. Here we set out to compare the function of PPARalpha in mouse and human hepatocytes via analysis of target gene regulation

Biomimetic mineralization of metal-organic frameworks as protective coatings for biomacromolecules

Enhancing the robustness of functional biomacromolecules is a critical challenge in biotechnology, which if addressed would enhance their use in pharmaceuticals, chemical processing and biostorage. Here we report a novel method, inspired by natural biomineralization processes, which provides unprecedented protection of biomacromolecules by encapsulating them within a class of porous materials termed metal-organic frameworks. We show that proteins, enzymes and DNA rapidly induce the formation of protective metal-organic framework coatings under physiological conditions by concentrating the framework building blocks and facilitating crystallization around the biomacromolecules. The resulting biocomposite is stable under conditions that would normally decompose many biological macromolecules. For example, urease and horseradish peroxidase protected within a metal-organic framework shell are found to retain bioactivity after being treated at 80 °C and boiled in dimethylformamide (153 °C), respectively. This rapid, low-cost biomimetic mineralization process gives rise to new possibilities for the exploitation of biomacromolecules.Kang Liang, Raffaele Ricco, Cara M. Doherty, Mark J. Styles, Stephen Bell, Nigel Kirby, Stephen Mudie, David Haylock, Anita J. Hill, Christian J. Doonan, Paolo Falcar

Differential effects of hypothalamic long-chain fatty acid infusions on suppression of hepatic glucose production

Our objective was to investigate whether the direct bilateral infusion of the monounsaturated fatty acid (MUFA) oleic acid (OA) within the mediobasal hypothalamus (MBH) is sufficient to reproduce the effect of administration of OA (30 nmol) in the third cerebral ventricle, which inhibits glucose production (GP) in rats. We used the pancreatic basal insulin clamp technique (plasma insulin ∼20 mU/ml) in combination with tracer dilution methodology to compare the effect of MBH OA on GP to that of a saturated fatty acid (SFA), palmitic acid (PA), and a polyunsaturated fatty acid (PUFA), linoleic acid (LA). The MBH infusion of 200 but not 40 pmol of OA was sufficient to markedly inhibit GP (by 61% from 12.6 ± 0.6 to 5.1 ± 1.6 mg·kg−1·min−1) such that exogenous glucose had to be infused at the rate of 6.0 ± 1.2 mg·kg−1·min−1 to prevent hypoglycemia. MBH infusion of PA also caused a significant decrease in GP, but only at a total dose of 4 nmol (GP 5.8 ± 1.6 mg·kg−1·min−1). Finally, MBH LA at a total dose of 0.2 and 4 nmol failed to modify GP compared with rats receiving MBH vehicle. Increased availability of OA within the MBH is sufficient to markedly inhibit GP. LA does not share the effect of OA, whereas PA can reproduce the potent effect of OA on GP, but only at a higher dose. It remains to be determined whether SFAs need to be converted to MUFAs to exert this effect or whether they activate a separate signaling pathway to inhibit GP

Optimization of Nonlinear Dose- and Concentration-Response Models Utilizing Evolutionary Computation

An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article’s potency (EC50) and efficacy (Emax) and variations in model fit may lead to different conclusions about an article’s performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data