Glycolate Oxidase Modulates Reactive Oxygen Species–Mediated Signal Transduction During Nonhost Resistance in Nicotiana benthamiana and Arabidopsis

In contrast to gene-for-gene disease resistance, nonhost resistance governs defense responses to a broad range of potential pathogen species. To identify specific genes involved in the signal transduction cascade associated with nonhost disease resistance, we used a virus-induced gene-silencing screen in Nicotiana benthamiana, and identified the peroxisomal enzyme glycolate oxidase (GOX) as an essential component of nonhost resistance. GOX-silenced N. benthamiana and Arabidopsis thaliana GOX T-DNA insertion mutants are compromised for nonhost resistance. Moreover, Arabidopsis gox mutants have lower H2O2 accumulation, reduced callose deposition, and reduced electrolyte leakage upon inoculation with hypersensitive response–causing nonhost pathogens. Arabidopsis gox mutants were not affected in NADPH oxidase activity, and silencing of a gene encoding NADPH oxidase (Respiratory burst oxidase homolog) in the gox mutants did not further increase susceptibility to nonhost pathogens, suggesting that GOX functions independently from NADPH oxidase. In the two gox mutants examined (haox2 and gox3), the expression of several defense-related genes upon nonhost pathogen inoculation was decreased compared with wild-type plants. Here we show that GOX is an alternative source for the production of H2O2 during both gene-for-gene and nonhost resistance responses

Mycobacterium tuberculosis CarD, an essential global transcriptional regulator forms amyloid-like fibrils

CarD is an essential global transcription regulator from Mycobacterium tuberculosis (Mtb) that binds RNA polymerase and activates transcription by stabilizing the transcription initiation complex. Available crystal structures have captured two distinct, monomeric and domain-swapped homodimeric, oligomeric states of CarD. However, the actual oligomeric state of CarD in solution and its biological relevance has remained unclear. Here, we confirm the presence of the homodimeric state of CarD in solution by using synchrotron-based small-angle X-ray scattering. Furthermore, by using biochemical and biophysical experiments, in addition to mass-spectrometry, transmission electron microscopy, and confocal imaging, we show that CarD is the first soluble cytosolic protein in Mtb which displays the tendency to form amyloid-like fibrils both in vitro as well as in vivo. We demonstrate that the deletion of the fourteen N-terminal residues involved in domain-swapping hampers amyloid formation, thus, suggesting that domain-swapping is crucial in amyloidogenesis. The discovery of the amyloidogenic property of an essential cytosolic global transcription regulator, CarD, in a pathogenic bacteria will further open up new frontiers in research.Peer reviewe

Developmental and Molecular Changes Underlying the Vernalization-Induced Transition to Flowering in Aquilegia coerulea (James)

Reproductive success in plants is dependent on many factors but the precise timing of flowering is certainly among the most crucial. Perennial plants often have a vernalization or over-wintering requirement in order to successfully flower in the spring. The shoot apical meristem undergoes drastic developmental and molecular changes as it transitions into inflorescence meristem (IM) identity, which then gives rise to floral meristems (FMs). In this study, we have examined the developmental and gene expression changes underlying the transition from the vegetative to reproductive phases in the basal eudicot Aquilegia coerulea, which has evolved a vernalization response independently relative to other established model systems. Results from both our histology and scanning electron studies demonstrate that developmental changes in the meristem occur gradually during the third and fourth weeks of vernalization. Based on RNAseq data and cluster analysis, several known flowering time loci, including AqFT and AqFL1, exhibit dramatic changes in expression during the fourth week. Further consideration of candidate gene homologs as well as unexpected loci of interest creates a framework in which we can begin to explore the genetic basis of the flowering time transition in Aquilegia

GENERAL CONTROL NONREPRESSIBLE4 Degrades 14-3-3 and the RIN4 Complex to Regulate Stomatal Aperture with Implications on Nonhost Disease Resistance and Drought Tolerance

Plants have complex and adaptive innate immune responses against pathogen infections. Stomata are key entry points for many plant pathogens. Both pathogens and plants regulate stomatal aperture for pathogen entry and defense, respectively. Not all plant proteins involved in stomatal aperture regulation have been identified. Here, we report GENERAL CONTROL NONREPRESSIBLE4 (GCN4), an AAA+-ATPase family protein, as one of the key proteins regulating stomatal aperture during biotic and abiotic stress. Silencing of GCN4 in Nicotiana benthamiana and Arabidopsis thaliana compromises host and nonhost disease resistance due to open stomata during pathogen infection. AtGCN4 overexpression plants have reduced H+-ATPase activity, stomata that are less responsive to pathogen virulence factors such as coronatine (phytotoxin produced by the bacterium Pseudomonas syringae) or fusicoccin (a fungal toxin produced by the fungus Fusicoccum amygdali), reduced pathogen entry, and enhanced drought tolerance. This study also demonstrates that AtGCN4 interacts with RIN4 and 14-3-3 proteins and suggests that GCN4 degrades RIN4 and 14-3-3 proteins via a proteasome-mediated pathway and thereby reduces the activity of the plasma membrane H+-ATPase complex, thus reducing proton pump activity to close stomata

Formate Dehydrogenase (FDH1) Localizes to Both Mitochondria and Chloroplast to Play a Role in Host and Nonhost Disease Resistance

Nonhost disease resistance is the most common type of plant defense mechanism against potential pathogens. In this study, the metabolic enzyme formate dehydrogenase (FDH1) was identified to be involved in nonhost disease resistance in Nicotiana benthamiana and Arabidopsis thaliana. In Arabidopsis, AtFDH1 was highly upregulated in response to both host and nonhost bacterial pathogens. Arabidopsis Atfdh1 mutants were compromised in nonhost resistance, basal resistance, and gene-for-gene resistance. The expression patterns of salicylic acid (SA) and jasmonic acid (JA) marker genes after pathogen infections in Atfdh1 mutant indicated that SA is most likely involved in the FDH1-mediated plant defense response to both host and nonhost bacterial pathogens. Previous studies reported that FDH1 localizes to only mitochondria, or both mitochondria and chloroplasts. Our results showed that the AtFDH1 localized to mitochondria and the amount of FDH1 localized to mitochondria increased upon infection with host or nonhost pathogens. Interestingly, the subcellular localization of FDH1 was observed in both mitochondria and chloroplasts after infection with a nonhost pathogen in Arabidopsis. We speculate that FDH1 plays a role in cellular signaling networks between mitochondria and chloroplasts to produce coordinated defense responses such as SA-induced reactive oxygen species (ROS) generation and hypersensitive response (HR)-induced cell death against nonhost bacterial pathogens

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

PHDcleav: A SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors

Background: Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes.Results: In this study, a novel method has been developed to predict Dicer cleavage sites on pre-miRNAs using Support Vector Machine. We used the dataset of experimentally validated human miRNA hairpins from miRBase, and extracted fourteen nucleotides around Dicer cleavage sites. We developed number of models using various types of features and achieved maximum accuracy of 66% using binary profile of nucleotide sequence taken from 5p arm of hairpin. The prediction performance of Dicer cleavage site improved significantly from 66% to 86% when we integrated secondary structure information. This indicates that secondary structure plays an important role in the selection of cleavage site. All models were trained and tested on 555 experimentally validated cleavage sites and evaluated using 5-fold cross validation technique. In addition, the performance was also evaluated on an independent testing dataset that achieved an accuracy of ~82%.Conclusion: Based on this study, we developed a webserver PHDcleav (http://www.imtech.res.in/raghava/phdcleav/) to predict Dicer cleavage sites in pre-miRNA. This tool can be used to investigate functional consequences of genetic variations/SNPs in miRNA on Dicer cleavage site, and gene silencing. Moreover, it would also be useful in the discovery of miRNAs in human genome and design of Dicer specific pre-miRNAs for potent gene silencing.Peer reviewedBiochemistry and Molecular Biolog

TESTLoc: protein subcellular localization prediction from EST data

Abstract Background The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes. Results We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%). Conclusions TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html</p