RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information

The attainment of complete map-based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. Identifying the localization of encoded proteins is the key to understanding their functional characteristics and facilitating their purification. Our proposed method, RSLpred, is an effort in this direction for genome-scale subcellular prediction of encoded rice proteins. First, the support vector machine (SVM)-based modules have been developed using traditional amino acid-, dipeptide- (i+1) and four parts-amino acid composition and achieved an overall accuracy of 81.43, 80.88 and 81.10%, respectively. Secondly, a similarity search-based module has been developed using position-specific iterated-basic local alignment search tool and achieved 68.35% accuracy. Another module developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix achieved an accuracy of 87.10%. In this study, a large number of modules have been developed using various encoding schemes like higher-order dipeptide composition, N- and C-terminal, splitted amino acid composition and the hybrid information. In order to benchmark RSLpred, it was tested on an independent set of rice proteins where it outperformed widely used prediction methods such as TargetP, Wolf-PSORT, PA-SUB, Plant-Ploc and ESLpred. To assist the plant research community, an online web tool 'RSLpred' has been developed for subcellular prediction of query rice proteins, which is freely accessible at http://www.imtech.res.in/raghava/rslpred

Oxidative Stress and Modulatory effects of the root extract of Phlogacanthus tubiflorus on the activity of Glutathione-S-Transferase in Hydrogen Peroxide treated Lymphocyte

Glutathione-S-transferase is one of the important enzyme systems that plays vital role in decomposition of lipid hydro-peroxides formed due to oxidative stress. In the present study GST activity increased in the lymphocytes treated with increasing concentration of H2O2, and decrease in the levels of GSH was observed. For similar treatment conditions LDH activity and MDA levels increased significantly leading to decrease in the cell viability. Treatment of lymphocytes with the root extract of Phlogacanthus tubiflorus (PTE) resulted in dose dependent decline in the GST activity and rise in GSH levels. LDH activity and MDA levels also declined that led to the increase of cell viability. Lymphocytes pre-treated with the PTE followed by H2O2 (0.1 and 1%) treatment, decline in the activity of GST and increase in GSH levels was observed. Also we have observed decline in the activity of LDH and MDA levels in the lymphocytes for both 0.1 and 1% of H2O2 though the magnitude of change was higher in the lymphocytes pre-treated with the PTE followed with 1% of H2O2 treatment. Significant increase in the cell viability for similar conditions was also observed. These findings suggest protective function of the root extracts might be through modulation of GST activity and levels of GSH and might find application in Chemomodulation in future

Mobile Robotic Platform to Gathering Real Time Sensory Data in Wireless Personal Area Network using Zigbee Transceiver Module

Robots are intelligent and obedient but impersonal machine which perform functions according to their own intelligence. Now days monitoring and control plays very vital role in every sectors .we can able to provide wirelessly controlled robots using Wireless personal Area Network (WPAN) interface with the help of Graphical User Interface (GUI) of MATLAB, which are capable of detecting  the gas contents and temperature and send it back to the control room. Now the data which is now available in the receiving end determines the further action of robots. The whole process is cost effective and economically sound. Further these wireless robots have broad applications like in wireless home security applications, coal mining, spy and war robots as they can make through in enemy areas just to track their activities. Other applications  like in Nuclear Power Plant we can send them in Radioactive area toanalyze things which is normally not possible for humans. Keywords:Wpan, Zigbbe, Matlab Gu

Expression of the High-Affinity K+ Transporter 1 (PpHKT1) Gene From Almond Rootstock ‘Nemaguard’ Improved Salt Tolerance of Transgenic Arabidopsis

Soil salinity affects plant growth and development, which directly impact yield. Plants deploy many mechanisms to cope with, or mitigate, salt stress. One of such mechanism is to control movement of ions from root to shoot by regulating the loading of Na+ in the transpiration stream. The high-affinity K+ transporter 1 (HKT1) is known to play a role in the removal of Na+from the xylem and bring it back to the root. As almond is a salt-sensitive crop, the rootstock plays an important role in successful almond cultivation in salt-affected regions. We currently lack knowledge on the molecular mechanisms involved in salt tolerance of almond rootstocks. In this study, we complemented the Arabidopsis athkt1 knockout mutant with HKT1 ortholog (PpHKT1) from the almond rootstock ‘Nemaguard’. Arabidopsis transgenic lines that were generated in athkt1 background with the constitutive promoter (PpHKT1OE2.2) and the native promoter (PpHKT1NP6) were subjected to different salt treatments. Both transgenic lines survived salt concentrations up to 120 mM NaCl, however, the mutant athkt1 died after 18 days under 120 mM NaCl. At 90 mM NaCl, the dry weight of athkt1 decreased significantly compared to the transgenic lines. Both transgenic lines showed significantly longer lateral roots compared to the athkt1 mutant at 80 mM NaCl treatment. The transgenic lines, PpHKT1OE2.2 and PpHKTNP6 had lower electrolyte leakage and higher relative water content compared to athkt1, suggesting that transgenic plants coped well with increased salt concentration by maintaining the integrity of the membranes. The expression analyses showed that PpHKT1 was induced in PpHKT1OE2.2 and PpHKTNP6 lines under salt treatment, which confirmed that both over-expression and native expression of PpHKT1 in the Arabidopsis mutant can complement salt tolerance function

Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches

Background: Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.Results: In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus.Conclusion: The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.Peer reviewedNational Institute for Microbial Forensics and Food and Agricultural BiosecurityBiochemistry and Molecular Biolog

Drought Stress Acclimation Imparts Tolerance to Sclerotinia sclerotiorum and Pseudomonas syringae in Nicotiana benthamiana

Acclimation of plants with an abiotic stress can impart tolerance to some biotic stresses. Such a priming response has not been widely studied. In particular, little is known about enhanced defense capacity of drought stress acclimated plants to fungal and bacterial pathogens. Here we show that prior drought acclimation in Nicotiana benthamiana plants imparts tolerance to necrotrophic fungus, Sclerotinia sclerotiorum, and also to hemi-biotrophic bacterial pathogen, Pseudomonas syringae pv. tabaci. S. sclerotiorum inoculation on N. benthamiana plants acclimated with drought stress lead to less disease-induced cell death compared to non-acclimated plants. Furthermore, inoculation of P. syringae pv. tabaci on N. benthamiana plants acclimated to moderate drought stress showed reduced disease symptoms. The levels of reactive oxygen species (ROS) in drought acclimated plants were highly correlated with disease resistance. Further, in planta growth of GFPuv expressing P. syringae pv. tabaci on plants pre-treated with methyl viologen showed complete inhibition of bacterial growth. Taken together, these experimental results suggested a role for ROS generated during drought acclimation in imparting tolerance against S. sclerotiorum and P. syringae pv. tabaci. We speculate that the generation of ROS during drought acclimation primed a defense response in plants that subsequently caused the tolerance against the pathogens tested

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

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

Background: Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning.Results: In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction accuracy for distinguishing plastid vs. non-plastids and only 20% in classifying various plastid-types, indicating the need and importance of machine learning algorithms.Conclusion: The current work is a first attempt to develop a methodology for classifying various plastid-type proteins. The prediction modules have also been made available as a web tool, PLpred available at http://bioinfo.okstate.edu/PLpred/ for real time identification/characterization. We believe this tool will be very useful in the functional annotation of various genomes.Peer reviewedNational Institute for Microbial Forensics and Food and Agricultural BiosecurityBiochemistry and Molecular Biolog

LacSubPred: Predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches

Background: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases.Results: In this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%.Conclusion: This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred).Peer reviewedNational Institute for Microbial Forensics and Food and Agricultural BiosecurityBiochemistry and Molecular Biolog

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