Lipopolysaccharide biosynthesis genes in koala type I Chlamydia: Cloning and characterization

We showed in 1988 that there are two strains of Chlamydia psittaci which infect the koala (Phascolarctos cinereus). In order to further investigate the role of these chlamydial strains in pathogenesis, we have attempted to identify genes of koala type I strain chlamydial which are involved in the immunogenic response, Transformation of Escherichia coli with a plasmid containing a 6.3-kb fragment (pKOC-10) of C. psittaci DNA caused the appearance of a specific chlamydial lipopolysaccharide (LPS) epitope on the host strain. The smallest DNA fragment capable of inducing the expression of chlamydial LPS was an Xbal fragment, 2.4 kb in size (pKOC-5). DNA sequence analysis of the complete fragment revealed regions of high identity, at the amino acid level, to the gseA genes of C. pneomoniae, C. psittaci 6BC and C. trachomatis, and the kdtA gene of E. coli which code for transferases catalysing the addition of 3-deoxy-D-manno-octulosonic acid (Kdo) residues to lipid A. Two open reading frames (ORFs) of 1,314 and 501 nucleotides in size, within the 2.4-kb fragment, were evident, and mRNA species corresponding to these ORFs were detected by Northern analysis. Both ORF1 and ORF2 are required for the appearance of chlamydia-specific LPS on the surface of recombinant E. coli.

Different transcriptional control of metabolism and extracellular matrix in visceral and subcutaneous fat of obese and rimonabant treated mice.

BACKGROUND: The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined. METHODOLOGY: C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant. PRINCIPAL FINDINGS: In VAT, "metabolic" genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas "structure" genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified "metabolic" and "structure" genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT. CONCLUSION: VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.

Extensions to Gene Set Enrichment

Motivation: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture moderate but coordinated changes in the expression of sets of functionally related genes. We propose number of extensions to GSEA, which uses different statistics to describe the association between genes and phenotype of interest. We make use of dimension reduction procedures, such as principle component analysis to identify gene sets containing coordinated genes. We also address the problem of overlapping among gene sets in this paper. Results: We applied our methods to the data come from a clinical trial in acute lymphoblastic leukemia (ALL) [1]. We identified interesting gene sets using different statistics. We find that gender may have effects on the gene expression in addition to the phenotype effects. Investigating overlap among interesting gene sets indicate that overlapping could alter the interpretation of the significant results.

GENE SET ENRICHMENT ANALYSIS MADE SIMPLE

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, chose an appropriate cut-off, and create a list of candidate genes. This approach has been criticized for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, many of these methods seem overly complicated. Furthermore, the most popular method, Gene Set Enrichment Analysis (GSEA), is based on a statistical test known for its lack of sensitivity. In this paper we compare the performance of a simple alternative to GSEA.We find that this simple solution clearly outperforms GSEA.We demonstrate this with eight different microarray datasets.