Regulation of phenylacetic acid degradation genes of Burkholderia cenocepacia K56-2

Background  

Metabolically versatile soil bacteria Burkholderia cepacia complex (Bcc) have emerged as opportunistic pathogens, especially of cystic fibrosis (CF). Previously, we initiated the characterization of the phenylacetic acid (PA) degradation pathway in B. cenocepacia, a member of the Bcc, and demonstrated the necessity of a functional PA catabolic pathway for full virulence in Caenorhabditis elegans. In this study, we aimed to characterize regulatory elements and nutritional requirements that control the PA catabolic genes in B. cenocepacia K56-2.     

Optimal parameter settings for information processing in gene regulatory networks

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.     

A novel and versatile computational tool to model translation

MOTIVATION: Much is now known about the mechanistic details of gene translation. There are also rapid advances in high-throughput technologies to determine quantitative aspects of the system. As a consequence-realistic and system-wide simulation models of translation are now feasible. Such models are also needed as devices to integrate a large volume of highly fragmented data known about translation. Software: In this application note, we present a novel, highly efficient software tool to model translation. The tool represents the main aspects of translation. Features include a representation of exhaustible tRNA pools, ribosome-ribosome interactions and differential initiation rates for different mRNA species. The tool is written in Java, and is hence portable and can be parameterized for any organism. AVAILABILITY: The model can be obtained from the authors or directly downloaded from the authors' home-page (http://goo.gl/JUWvI).     

Type I interferon is required for T helper (Th) 2 induction by dendritic cells

Type 2 inflammation is a defining feature of infection with parasitic worms (helminths), as well as being responsible for widespread suffering in allergies. However, the precise mechanisms involved in T helper (Th) 2 polarization by dendritic cells (DCs) are currently unclear. We have identified a previously unrecognized role for type I IFN (IFN‐I) in enabling this process. An IFN‐I signature was evident in DCs responding to the helminth Schistosoma mansoni or the allergen house dust mite (HDM). Further, IFN‐I signaling was required for optimal DC phenotypic activation in response to helminth antigen (Ag), and efficient migration to, and localization with, T cells in the draining lymph node (dLN). Importantly, DCs generated from Ifnar1^?/? mice were incapable of initiating Th2 responses in vivo. These data demonstrate for the first time that the influence of IFN‐I is not limited to antiviral or bacterial settings but also has a central role to play in DC initiation of Th2 responses.     

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

Background  

The sequencing of the human genome has enabled us to access a comprehensive list of genes (both experimental and predicted) for further analysis. While a majority of the approximately 30000 known and predicted human coding genes are characterized and have been assigned at least one function, there remains a fair number of genes (about 12000) for which no annotation has been made. The recent sequencing of other genomes has provided us with a huge amount of auxiliary sequence data which could help in the characterization of the human genes. Clustering these sequences into families is one of the first steps to perform comparative studies across several genomes.