Summary The functions of phenylpropanoid compounds in plant defence range from preformed or inducible physical and chemical barriers against infection to signal molecules involved in local and systemic signalling for defence gene induction. Defensive functions are not restricted to a particular class of phenylpropanoid compound, but are found in the simple hydroxycinnamic acids and monolignols through to the more complex flavonoids, isoflavonoids, and stilbenes. The enzymatic steps involved in the biosynthesis of the major classes of phenylpropanoid compounds are now well established, and many of the corresponding genes have been cloned. Less is understood about the regulatory genes that orchestrate rapid, coordinated induction of phenylpropanoid defences in response to microbial attack. Many of the biosynthetic pathway enzymes are encoded by gene families, but the specific functions of individual family members remain to be determined. The availability of the complete genome sequence of Arabidopsis thaliana, and the extensive expressed sequence tag (EST) resources in other species, such as rice, soybean, barrel medic, and tomato, allow, for the first time, a full appreciation of the comparative genetic complexity of the phenylpropanoid pathway across species. In addition, gene expression array analysis and metabolic profiling approaches make possible comparative parallel analyses of global changes at the genome and metabolome levels, facilitating an understanding of the relationships between changes in specific transcripts and subsequent alterations in metabolism in response to infection.

BindN () takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.

BackgroundWRKY proteins are newly identified transcription factors involved in many plant processes including plant responses to biotic and abiotic stresses. To date, genes encoding WRKY proteins have been identified only from plants. Comprehensive search for WRKY genes in non-plant organisms and phylogenetic analysis would provide invaluable information about the origin and expansion of the WRKY family.ResultsWe searched all publicly available sequence data for WRKY genes. A single copy of the WRKY gene encoding two WRKY domains was identified from Giardia lamblia, a primitive eukaryote, Dictyostelium discoideum, a slime mold closely related to the lineage of animals and fungi, and the green alga Chlamydomonas reinhardtii, an early branching of plants. This ancestral WRKY gene seems to have duplicated many times during the evolution of plants, resulting in a large family in evolutionarily advanced flowering plants. In rice, the WRKY gene family consists of over 100 members. Analyses suggest that the C-terminal domain of the two-WRKY-domain encoding gene appears to be the ancestor of the single-WRKY-domain encoding genes, and that the WRKY domains may be phylogenetically classified into five groups. We propose a model to explain the WRKY family's origin in eukaryotes and expansion in plants.ConclusionsWRKY genes seem to have originated in early eukaryotes and greatly expanded in plants. The elucidation of the evolution and duplicative expansion of the WRKY genes should provide valuable information on their functions.

Tall fescue (Festuca arundinacea Schreb.) is a major cool season forage and turf grass in the temperate regions of the world. It is also a close relative of other important forage and turf grasses, including meadow fescue and the cultivated ryegrass species. Until now, no SSR markers have been developed from the tall fescue genome. We designed 157 EST-SSR primer pairs from tall fescue ESTs and tested them on 11 genotypes representing seven grass species. Nearly 92% of the primer pairs produced characteristic simple sequence repeat (SSR) bands in at least one species. A large proportion of the primer pairs produced clear reproducible bands in other grass species, with most success in the close taxonomic relatives of tall fescue. A high level of marker polymorphism was observed in the outcrossing species tall fescue and ryegrass (66%). The marker polymorphism in the self-pollinated species rice and wheat was low (43% and 38%, respectively). These SSR markers were useful in the evaluation of genetic relationships among the Festuca and Lolium species. Sequencing of selected PCR bands revealed that the nucleotide sequences of the forage grass genotypes were highly conserved. The two cereal species, particularly rice, had significantly different nucleotide sequences compared to the forage grasses. Our results indicate that the tall fescue EST-SSR markers are valuable genetic markers for the Festuca and Lolium genera. These are also potentially useful markers for comparative genomics among several grass species.

A survey of six organ-/tissue-specific proteomes of the model legume barrel medic (Medicago truncatula) was performed. Two-dimensional polyacrylamide gel electrophoresis reference maps of protein extracts from leaves, stems, roots, flowers, seed pods, and cell suspension cultures were obtained. Five hundred fifty-one proteins were excised and 304 proteins identified using peptide mass fingerprinting and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Nanoscale high-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry was used to validate marginal matrix-assisted laser desorption ionization time-of-flight mass spectrometry protein identifications. This dataset represents one of the most comprehensive plant proteome projects to date and provides a basis for future proteome comparison of genetic mutants, biotically and abiotically challenged plants, and/or environmentally challenged plants. Technical details concerning peptide mass fingerprinting, database queries, and protein identification success rates in the absence of a sequenced genome are reported and discussed. A summary of the identified proteins and their putative functions are presented. The tissue-specific expression of proteins and the levels of identified proteins are compared with their related transcript abundance as quantified through EST counting. It is estimated that approximately 50% of the proteins appear to be correlated with their corresponding mRNA levels.

The Snyder-Robinson syndrome is caused by missense mutations in the spermine sythase gene that encodes a protein (SMS) of 529 amino acids. Here we investigate, in silico, the molecular effect of three missense mutations, c.267G4A (p.G56S), c.496T4G (p.V132G), and c.550T4C (p.I150T) in SMS that were clinically identified to cause the disease. Single-point energy calculations, molecular dynamics simulations, and pKa calculations revealed the effects of these mutations on SMS's stability, flexibility, and interactions. It was predicted that the catalytic residue, Asp276, should be protonated prior binding the substrates. The pKa calculations indicated the p.I150T mutation causes pKa changes with respect to the wild-type SMS, which involve titratable residues interacting with the S-methyl-5 0 -thioadenosine (MTA) substrate. The p.I150T missense mutation was also found to decrease the stability of the C-terminal domain and to induce structural changes in the vicinity of the MTA binding site. The other two missense mutations, p.G56S and p.V132G, are away from active site and do not perturb its wild-type properties, but affect the stability of both the monomers and the dimer. Specifically, the p.G56S mutation is predicted to greatly reduce the affinity of monomers to form a dimer, and therefore should have a dramatic effect on SMS function because dimerization is essential for SMS activity.

Freely available upon request for academic and non-commercial use. Commercial use is available through licensing agreement http://www.noble.org/PlantBio/MS/MSFACTs/MSFACTs.html.

Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and their tissue and developmentally specific expression. LncRNA subcellular localization is highly informative regarding its biological function, although it is difficult to discover because few prediction methods currently exist. While protein subcellular localization prediction is a well-established research field, lncRNA localization prediction is a novel research problem. We developed DeepLncRNA, a deep learning algorithm which predicts lncRNA subcellular localization directly from lncRNA transcript sequences. We analyzed 93 strand-specific RNA-seq samples of nuclear and cytosolic fractions from multiple cell types to identify differentially localized lncRNAs. We then extracted sequence-based features from the lncRNAs to construct our DeepLncRNA model, which achieved an accuracy of 72.4%, sensitivity of 83%, specificity of 62.4% and area under the receiver operating characteristic curve of 0.787. Our results suggest that primary sequence motifs are a major driving force in the subcellular localization of lncRNAs.