Background: Glandular trichomes produce a wide variety of commercially important secondary metabolites in many plant species. The most prominent anti-malarial drug artemisinin, a sesquiterpene lactone, is produced in glandular trichomes of Artemisia annua. However, only limited genomic information is currently available in this non-model plant species. Results: We present a global characterization of A. annua glandular trichome transcriptome using 454 pyrosequencing. Sequencing runs using two normalized cDNA collections from glandular trichomes yielded 406,044 expressed sequence tags (average length = 210 nucleotides), which assembled into 42,678 contigs and 147,699 singletons. Performing a second sequencing run only increased the number of genes identified by ~30%, indicating that massively parallel pyrosequencing provides deep coverage of the A. annua trichome transcriptome. By BLAST search against the NCBI non-redundant protein database, putative functions were assigned to over 28,573 unigenes, including previously undescribed enzymes likely involved in sesquiterpene biosynthesis. Comparison with ESTs derived from trichome collections of other plant species revealed expressed genes in common functional categories across different plant species. RT-PCR analysis confirmed the expression of selected unigenes and novel transcripts in A. annua glandular trichomes. Conclusion: The presence of contigs corresponding to enzymes for terpenoids and flavonoids biosynthesis suggests important metabolic activity in A. annua glandular trichomes. Our comprehensive survey of genes expressed in glandular trichome will facilitate new gene discovery and shed light on the regulatory mechanism of artemisinin metabolism and trichome function in A. annua.

Background: Cavendish, the most widely grown banana cultivar, is relatively resistant to Race 1 ofFusarium oxysporumf. sp.cubense(Foc1) which caused widespread Panama disease during the first half of the 20^thcentury but is susceptible to Tropical Race 4 of Foc (Foc TR4) which is threatening world banana production. The genome of the diploid speciesMusa acuminatawhich is the ancestor of a majority of triploid banana cultivars has recently been sequenced. Availability of banana transcriptomes will be highly useful for improving banana genome annotation and for biological research. The knowledge of global gene expression patterns influenced by infection of different Foc races will help to understand the host responses to the infection.Results: RNA samples from different organs of the Cavendish cultivar were pooled for deep sequencing using the Illumina technology. Analysis of the banana transcriptome led to identification of over 842 genes that were not annotated by theMusagenome project. A large number of simple nucleotide polymorphisms (SNPs) and short insertions and deletion (indels) were identified from the transcriptome data. GFP-expressing Foc1 and Foc TR4 were used to monitor the infection process. Both Foc1 and Foc TR4 were found to be able to invade banana roots and spread to root vascular tissues in the first two days following inoculation. Digital gene expression (DGE) profiling analysis reveal that the infection by Foc1 and Foc TR4 caused very similar changes in the global gene expression profiles in the banana roots during the first two days of infection. The Foc infection led to induction of many well-known defense-related genes. Two genes encoding the ethylene biosynthetic enzyme ACC oxidase and several ethylene-responsive transcription factors (ERF) were among the strongly induced genes by both Foc1 and Foc TR4.Conclusions: Both Foc1 and Foc TR4 are able to spread into the vascular system of banana roots during the early infection process and their infection led to similar gene expression profiles in banana roots. The transcriptome profiling analysis indicates that the ethylene synthetic and signalling pathways were activated in response to the Foc infection.

Background: Plant roots are the primary site of perception and injury for saline-alkaline stress. The current knowledge of saline-alkaline stress transcriptome is mostly focused on saline (NaCl) stress and only limited information on alkaline (NaHCO₃) stress is available. Results: Using Affymetrix^® Soybean GeneChip^®, we conducted transcriptional profiling on Glycine soja roots subjected to 50 mmol/L NaHCO₃ treatment. In a total of 7088 probe sets, 3307 were up-regulated and 5720 were down-regulated at various time points. The number of significantly stress regulated genes increased dramatically after 3 h stress treatment and peaked at 6 h. GO enrichment test revealed that most of the differentially expressed genes were involved in signal transduction, energy, transcription, secondary metabolism, transporter, disease and defence response. We also detected 11 microRNAs regulated by NaHCO₃ stress. Conclusions: This is the first comprehensive wild soybean root transcriptome analysis under alkaline stress. These analyses have identified an inventory of genes with altered expression regulated by alkaline stress. The data extend the current understanding of wild soybean alkali stress response by providing a set of robustly selected, differentially expressed genes for further investigation.

Motivation: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. Results: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. Availability: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac. Contact:djguo@cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Redox regulation and signaling, which are involved in various cellular processes, have become one of the research focuses in the past decade. Cysteine thiol groups are particularly susceptible to post-translational modification, and their reversible oxidation is of critical role in redox regulation and signaling. With the tremendous improvement of techniques, hundreds of redox proteins along with their redox-sensitive cysteines have been reported, and the number is still fast growing. However, until now there is no database to accommodate the rapid accumulation of information on protein oxidative modification. Here we present RedoxDB—a manually curated database for experimentally validated redox proteins. RedoxDB (version 1.0) consists of two datasets (A and B, for proteins with or without verified modified cysteines, respectively) and includes 2157 redox proteins containing 2203 cysteine residues with oxidative modification. For each modified cysteine, the exact position, modification type and flanking sequence are provided. Additional information, including gene name, organism, sequence, literature references and links to UniProt and PDB, is also supplied. The database supports several functions including data search, blast and browsing. Bulk download of the entire dataset is also available. We expect that RedoxDB will be useful for both experimental studies and computational analyses of protein oxidative modification. Availability: The database is freely available at: http://biocomputer.bio.cuhk.edu.hk/RedoxDB. Contact:djguo@cuhk.edu.hk Supplementary Information:Supplementary data are available at Bioinformatics Online.

Disulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown. In this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides. By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation. This study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two groups of disulfides were identified, and a SVM-based classifier for predicting reversible disulfides was developed accordingly. A web server named RevssPred can be accessed freely from: http://biocomputer.bio.cuhk.edu.hk/RevssPred .