We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family.

The 1,000 plants (1KP) project is an international multi-disciplinary consortium that has generated transcriptome data from over 1,000 plant species,

Summary: Pseudomonas aeruginosa, the principal pathogen of cystic fibrosis patients, forms antibiotic‐resistant biofilms promoting chronic colonization of the airways. The extracellular (EPS) matrix is a crucial component of biofilms that provides the community multiple benefits. Recent work suggests that the secondary messenger, cyclic‐di‐GMP, promotes biofilm formation. An analysis of factors specifically expressed in P. aeruginosa under conditions of elevated c‐di‐GMP, revealed functions involved in the production and maintenance of the biofilm extracellular matrix. We have characterized one of these components, encoded by the PA4625 gene, as a putative adhesin and designated it cdrA. CdrA shares structural similarities to extracellular adhesins that belong to two‐partner secretion systems. The cdrA gene is in a two gene operon that also encodes a putative outer membrane transporter, CdrB. The cdrA gene encodes a 220 KDa protein that is predicted to be rod‐shaped protein harbouring a β‐helix structural motif. Western analysis indicates that the CdrA is produced as a 220 kDa proprotein and processed to 150 kDa before secretion into the extracellular medium. We demonstrated that cdrAB expression is minimal in liquid culture, but is elevated in biofilm cultures. CdrAB expression was found to promote biofilm formation and auto‐aggregation in liquid culture. Aggregation mediated by CdrA is dependent on the Psl polysaccharide and can be disrupted by adding mannose, a key structural component of Psl. Immunoprecipitation of Psl present in culture supernatants resulted in co‐immunoprecipitation of CdrA, providing additional evidence that CdrA directly binds to Psl. A mutation in cdrA caused a decrease in biofilm biomass and resulted in the formation of biofilms exhibiting decreased structural integrity. Psl‐specific lectin staining suggests that CdrA either cross‐links Psl polysaccharide polymers and/or tethers Psl to the cells, resulting in increased biofilm structural stability. Thus, this study identifies a key protein structural component of the P. aeruginosa EPS matrix.

The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates—effector proteins—are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal. Pathogenic bacteria release a number of different proteins that function to interfere with host defenses and allow bacterial invasion, persistence, and replication in the host. In many bacterial pathogens, the type III secretion system is used to inject these virulence factors directly to the cytoplasm of the host cell. The secreted proteins do not have well-conserved sequences and do not have any kind of common identifiable signal sequence to target them for secretion. This makes it very difficult to identify secreted proteins of this kind without experimental investigation, as can be done in other secretion systems. In this study, we develop a computational approach to detect secreted virulence factors from genomic protein sequences. We use this method to compare the N-terminal regions of proteins from S. Typhimurium and a plant pathogen, P. syringae, and show that this approach is the most effective method of computational identification of type III secreted proteins to date. We further use this approach to identify a sequence pattern in these proteins that presumably helps direct virulence proteins to the type III secretion apparatus. We provide novel predictions of secreted proteins in these two organisms, as well as in the human pathogen C. trachomatis. Better understanding of secreted virulence factors in pathogens will lead to new ways of combating important infectious diseases and provide understanding of the complex interaction between pathogen and host.