Identification and physical organization of the gene cluster involved in the biosynthesis of Burkholderia cepacia complex exopolysaccharide

Bacteria belonging to the Burkholderia cepacia complex (BCC) are important opportunistic pathogens in patients with cystic fibrosis (CF). Since approximately 80% of the CF isolates examined produce exopolysaccharide (EPS), it was hypothesized that this EPS may play a role in the colonization and persistence of these bacteria in the CF lung. The present study describes the identification and physical organization of the EPS biosynthetic gene cluster. This bce gene cluster was identified following the isolation of three EPS-defective mutants from the highly mucoid CF isolate IST408, belonging to BCC genomovar I, based on random plasposon insertion mutagenesis and comparison of the nucleotide sequence of the interrupted genes with the available genome of Burkholderia cenocepacia J2315. This 16.2 kb cluster includes 12 genes and is located on chromosome 2. Database searches for homologous proteins and secondary structure analysis for the deduced Bce amino acid sequences revealed genes predicted to encode enzymes required for the formation of nucleotide sugar precursors, glycosyltransferases involved in the repeat-unit assembly, and other proteins involved in polymerization and export of bacterial surface polysaccharides.     

Sequence divergence in type III secretion gene clusters of the Burkholderia cepacia complex

The Burkholderia cepacia complex (BCC) comprises a group of bacteria associated with opportunistic infections, especially in cystic fibrosis patients. B. cenocepacia J2315, of the transmissible ET12 lineage, contains a type III secretion (TTS) gene cluster implicated in pathogenicity. PCR and hybridisation assays indicate that the TTS gene cluster is present in all members of the BCC except B. cepacia (formerly genomovar I). The TTS gene clusters of B. cenocepacia J2315 and B. multivorans are similar in organisation but have variable levels of gene identity. Nucleotide sequence data obtained for the equivalent region of the B. cepacia genome indicate the absence of TTS structural genes due to a rearrangement likely to involve more than one step.     

Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases

We have generated a novel, neuro-specific ncRNA microarray, covering 1472 ncRNA species, to investigate their expression in different mouse models for central nervous system diseases. Thereby, we analyzed ncRNA expression in two mouse models with impaired calcium channel activity, implicated in Epilepsy or Parkinson''s disease, respectively, as well as in a mouse model mimicking pathophysiological aspects of Alzheimer''s disease. We identified well over a hundred differentially expressed ncRNAs, either from known classes of ncRNAs, such as miRNAs or snoRNAs or which represented entirely novel ncRNA species. Several differentially expressed ncRNAs in the calcium channel mouse models were assigned as miRNAs and target genes involved in calcium signaling, thus suggesting feedback regulation of miRNAs by calcium signaling. In the Alzheimer mouse model, we identified two snoRNAs, whose expression was deregulated prior to amyloid plaque formation. Interestingly, the presence of snoRNAs could be detected in cerebral spine fluid samples in humans, thus potentially serving as early diagnostic markers for Alzheimer''s disease. In addition to known ncRNAs species, we also identified 63 differentially expressed, entirely novel ncRNA candidates, located in intronic or intergenic regions of the mouse genome, genomic locations, which previously have been shown to harbor the majority of functional ncRNAs.     

Computational methods in noncoding RNA research

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure. During the execution of this work, AML was supported by CAPES fellowship.     

Computational RNomics: Structure identification and functional prediction of non-coding RNAs <Emphasis Type="Italic">in silico</Emphasis>

The eukaryotic genome contains varying numbers of non-coding RNA(ncRNA) genes.Computational RNomics takes a multidisciplinary approach,like information science,to resolve the structure and function of ncRNAs.Here,we review the main issues in Computational RNomics of data storage and management,ncRNA gene identification and characterization,ncRNA target identification and functional prediction,and we summarize the main methods and current content of computational RNomics.     

Computational RNomics:Structure identification and functional prediction of non-coding RNAs in silico

The eukaryotic genome contains varying numbers of non-coding RNA(ncRNA) genes."Computational RNomics" takes a multidisciplinary approach,like information science,to resolve the structure and function of ncRNAs.Here,we review the main issues in "Computational RNomics" of data storage and management,ncRNA gene identification and characterization,ncRNA target identification and functional prediction,and we summarize the main methods and current content of "computational RNomics".     

Plant noncoding RNA gene discovery by "single-genome comparative genomics"

Plant genomes have undergone multiple rounds of duplications that contributed massively to the growth of gene families. The structure of resulting families has been studied in depth for protein-coding genes. However, little is known about the impact of duplications on noncoding RNA (ncRNA) genes. Here we perform a systematic analysis of duplicated regions in the rice genome in search of such ncRNA repeats. We observe that, just like their protein counterparts, most ncRNA genes have undergone multiple duplications that left visible sequence conservation footprints. The extent of ncRNA gene duplication in plants is such that these sequence footprints can be exploited for the discovery of novel ncRNA gene families on a large scale. We developed an SVM model that is able to retrieve likely ncRNA candidates among the 100,000+ repeat families in the rice genome, with a reasonably low false-positive discovery rate. Among the nearly 4000 ncRNA families predicted by this means, only 90 correspond to putative snoRNA or miRNA families. About half of the remaining families are classified as structured RNAs. New candidate ncRNAs are particularly enriched in UTR and intronic regions. Interestingly, 89% of the putative ncRNA families do not produce a detectable signal when their sequences are compared to another grass genome such as maize. Our results show that a large fraction of rice ncRNA genes are present in multiple copies and are species-specific or of recent origin. Intragenome comparison is a unique and potent source for the computational annotation of this major class of ncRNA.