Fenofibrate, a peroxisome proliferator-activated receptor-alpha agonist, blocks steatosis and alters the inflammatory response in a mouse model of inflammation-dioxin interaction.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (dioxin; TCDD) is an environmental contaminant that elicits a variety of toxic effects, many of which are mediated through activation of the aryl hydrocarbon receptor (AhR). Interaction between AhR and the peroxisome proliferator-activated receptor-alpha (PPAR-α), which regulates fatty acid metabolism, has been suggested. Furthermore, with recognition of the prevalence of inflammatory conditions, there is current interest in the potential for inflammatory stress to modulate the response to environmental agents. The aim of this work was to assess the interaction of TCDD with hepatic inflammation modulated by fenofibrate, a PPAR-α agonist. Female, C57BL/6 mice were treated orally with vehicle or fenofibrate (250 mg/kg) for 13 days, and then were given vehicle or 30 µg/kg TCDD. Four days later, the animals received an i.p. injection of lipopolysaccharide-galactosamine (LPS-GalN) (0.05x107 EU/kg and 500 mg/kg, respectively) to incite inflammation, or saline as vehicle control. After 4 h, the mice were euthanized, and blood and liver samples were collected for analysis. Livers of animals treated with TCDD with or without LPS-GalN had increased lipid deposition, and this effect was blocked by fenofibrate. In TCDD/LPS-GalN-treated mice, fenofibrate caused an increase in plasma activity of alanine aminotransferase, a marker of hepatocellular injury. TCDD reduced LPS-GalN-induced apoptosis, an effect that was prevented by fenofibrate pretreatment. LPS-GalN induced an increase in the concentration of interleukin-6 in plasma and accumulation of neutrophils in liver. TCDD exposure enhanced the former response and inhibited the latter one. These results suggest that fenofibrate counteracts the changes in lipid metabolism induced by TCDD but increases inflammation and liver injury in this model of inflammation-TCDD interaction.

Trovafloxacin enhances lipopolysaccharide-stimulated production of tumor necrosis factor-α by macrophages: role of the DNA damage response.

Trovafloxacin (TVX) is a drug that has caused idiosyncratic, drug-induced liver injury (IDILI) in humans. In a murine model of IDILI, otherwise nontoxic doses of TVX and the inflammagen lipopolysaccharide (LPS) interacted to produce pronounced hepatocellular injury. The liver injury depended on a TVX-induced, small but significant prolongation of tumor necrosis factor-α (TNF) appearance in the plasma. The enhancement of TNF expression by TVX was reproduced in vitro in RAW 264.7 murine macrophages (RAW cells) stimulated with LPS. The current study was designed to identify the molecular target of TVX responsible for this response in RAW cells. An in silico analysis suggested a favorable binding profile of TVX to eukaryotic topoisomerase II-α (TopIIα), and a cell-free assay revealed that TVX inhibited eukaryotic TopIIα activity. Topoisomerase inhibition is known to lead to DNA damage, and TVX increased the DNA damage marker phosphorylated histone 2A.X in RAW cells. Moreover, TVX induced activation of the DNA damage sensor kinases, ataxia telangiectasia mutated (ATM) and Rad3-related (ATR). The ATR inhibitor NU6027 [6-(cyclohexylmethoxy)-5-nitrosopyrimidine-2,4-diamine] prevented the TVX-mediated increases in LPS-induced TNF mRNA and protein release, whereas a selective ATM inhibitor [2-(4-morpholinyl)-6-(1-thianthrenyl)-4H-pyran-4-one (KU55933)] was without effect. TVX prolonged TNF mRNA stability, and this effect was largely attenuated by NU6027. These results suggest that TVX can inhibit eukaryotic topoisomerase, leading to activation of ATR and potentiation of TNF release by macrophages, at least in part through increased mRNA stability. This off-target effect might contribute to the ability of TVX to precipitate IDILI in humans.

A workflow learning model to improve geovisual analytics utility.

INTRODUCTION: This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. OBJECTIVES: The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. METHODOLOGY: The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on the concept of scientific workflows. Second, we implemented an interface in the G-EX Portal Learn Module to demonstrate the workflow learning model. The workflow interface allows users to drag learning artifacts uploaded to the G-EX Portal onto a central whiteboard and then annotate the workflow using text and drawing tools. Once completed, users can visit the assembled workflow to get an idea of the kind, number, and scale of analysis steps, view individual learning artifacts associated with each node in the workflow, and ask questions about the overall workflow or individual learning artifacts through the associated forums. An example learning workflow in the domain of epidemiology is provided to demonstrate the effectiveness of the approach. RESULTS/CONCLUSIONS: In the context of geovisual analytics, GIScientists are not only responsible for developing software to facilitate visually-mediated reasoning about large and complex spatiotemporal information, but also for ensuring that this software works. The workflow learning model discussed in this paper and demonstrated in the G-EX Portal Learn Module is one approach to improving the utility of geovisual analytics software. While development of the G-EX Portal Learn Module is ongoing, we expect to release the G-EX Portal Learn Module by Summer 2009.