Tetradecylthioacetic Acid Increases Hepatic Mitochondrial β-Oxidation and Alters Fatty Acid Composition in a Mouse Model of Chronic Inflammation

The administration of tetradecylthioacetic acid (TTA), a hypolipidemic and anti-inflammatory modified bioactive fatty acid, has in several experiments based on high fat diets been shown to improve lipid transport and utilization. It was suggested that increased mitochondrial and peroxisomal fatty acid oxidation in the liver of Wistar rats results in reduced plasma triacylglycerol (TAG) levels. Here we assessed the potential of TTA to prevent tumor necrosis factor (TNF) α-induced lipid modifications in human TNFα (hTNFα) transgenic mice. These mice are characterized by reduced β-oxidation and changed fatty acid composition in the liver. The effect of dietary treatment with TTA on persistent, low-grade hTNFα overexpression in mice showed a beneficial effect through decreasing TAG plasma concentrations and positively affecting saturated and monounsaturated fatty acid proportions in the liver, leading to an increased anti-inflammatory fatty acid index in this group. We also observed an increase of mitochondrial β-oxidation in the livers of TTA treated mice. Concomitantly, there were enhanced plasma levels of carnitine, acetyl carnitine, propionyl carnitine, and octanoyl carnitine, no changed levels in trimethyllysine and palmitoyl carnitine, and a decreased level of the precursor for carnitine, called γ-butyrobetaine. Nevertheless, TTA administration led to increased hepatic TAG levels that warrant further investigations to ascertain that TTA may be a promising candidate for use in the amelioration of inflammatory disorders characterized by changed lipid metabolism due to raised TNFα levels

Gene Ontology annotations and resources.

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system