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.     

Reproducible Clusters from Microarray Research: Whither?

Motivation

In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets.

Methods

We considered four commonly used iterative partitioning algorithms (Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. Cluster stability was quantified using Cramer's v ² from a kXk table. Cramer's v ² is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility.

Results

All four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: (1) microarray datasets lack natural clustering structure thereby producing low stability scores on all four methods; (2) the algorithms studied do not produce reliable results and/or (3) sample sizes typically used in microarray research may be too small to support derivation of reliable clustering results. Further research should be directed towards evaluating stability performances of more clustering algorithms on more datasets specially having larger sample sizes with larger numbers of clusters considered.

     

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.     

The IL-10 and IFN-γ pathways are essential to the potent immunosuppressive activity of cultured CD8+ NKT-like cells

Background  

CD8⁺ NKT-like cells are naturally occurring but rare T cells that express both T cell and natural killer cell markers. These cells may play key roles in establishing tolerance to self-antigens; however, their mechanism of action and molecular profiles are poorly characterized due to their low frequencies. We developed an efficient in vitro protocol to produce CD8⁺ T cells that express natural killer cell markers (CD8⁺ NKT-like cells) and extensively characterized their functional and molecular phenotypes using a variety of techniques.     

The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon

The microbiome is being characterized by large-scale sequencing efforts, yet it is not known whether it regulates host metabolism in a general versus tissue-specific manner or which bacterial metabolites are important. Here, we demonstrate that microbiota have a strong effect on energy homeostasis in the colon compared to other tissues. This tissue specificity is due to colonocytes utilizing bacterially produced butyrate as their primary energy source. Colonocytes from germfree mice are in an energy-deprived state and exhibit decreased expression of enzymes that catalyze key steps in intermediary metabolism including the TCA cycle. Consequently, there is a marked decrease in NADH/NAD(+), oxidative phosphorylation, and ATP levels, which results in AMPK activation, p27(kip1) phosphorylation, and autophagy. When butyrate is added to germfree colonocytes, it rescues their deficit in mitochondrial respiration and prevents them from undergoing autophagy. The mechanism is due to butyrate acting as an energy source rather than as an HDAC inhibitor.     

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.