Beneficial Plant-Microbe Interactions and Stress Tolerance in Maize

Beneficial microbes are crucial for improving crop adaptation and growth under various stresses. They enhance nutrient uptake, improve plant immune responses, and help plants tolerate stresses like drought, salinity, and heat. The yield potential of any crop is significantly influenced by its associated microbiomes and their potential to improve growth under different stressful environments. Therefore, it is crucial and exciting to understand the mechanisms of plant-microbe interactions. Maize (Zea mays L.) is one of the primary staple foods worldwide, in addition to wheat and rice. Maize is also an industrial crop globally, contributing 83% of its production for use in feed, starch, and biofuel industries. Maize requires significant nitrogen fertilization to achieve optimal growth and yield. Maize plants are highly susceptible to heat, salinity, and drought stresses and require innovative methods to mitigate the harmful effects of environmental stresses and reduce the use of chemical fertilizers. This review summarizes our current understanding of the beneficial interactions between maize plants and specific microbes. These beneficial microbes improve plant resilience to stress and increase productivity. For example, they regulate electron transport, downregulate catalase, and upregulate antioxidants. We also review the roles of plant growth-promoting rhizobacteria (PGPR) in enhancing stress tolerance in maize. Additionally, we explore the application of these microbes in maize production and identify major knowledge gaps that need to be addressed to utilize the potential of beneficial microbes fully

Enhanced oral bioavailability and hepatoprotective activity of thymoquinone in the form of phospholipidic nano-constructs

Background: The poor biopharmaceutical properties of thymoquinone (TQ) obstruct its development as a hepatoprotective agent. To surmount the delivery challenges of TQ, phospholipid nanoconstructs (PNCs) were constructed. Method: PNCs were constructed employing microemulsification technique and systematic optimization by three-factor three level Box-Behnken design. Result: Optimized PNC composition exhibited nano size (90%), controlled drug release pattern, and neutral surface charge (zeta potential of −0.65 mV). After oral administration of a single dose of PNC, it showed a relative bioavailability of 386.03% vis-à-vis plain TQ suspension. Further, TQ-loaded PNC demonstrated significant enhanced hepato-protective effect vis-à-vis pure TQ suspension and silymarin, as evidenced by reduction in the ALP, ALT, AST, bilirubin, and albumin level and ratified by histopathological analysis. Conclusion: TQ-loaded PNCs can be efficient nano-platforms for the management of hepatic disorders and promising drug delivery systems to enhance oral bioavailability of this hydrophobic molecule

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

PRMT Blockade Induces Defective DNA Replication Stress Response and Synergizes with PARP Inhibition

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition

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

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

Neuronal Sirt3 Protects against Excitotoxic Injury in Mouse Cortical Neuron Culture

BACKGROUND: Sirtuins (Sirt), a family of nicotinamide adenine nucleotide (NAD) dependent deacetylases, are implicated in energy metabolism and life span. Among the known Sirt isoforms (Sirt1-7), Sirt3 was identified as a stress responsive deacetylase recently shown to play a role in protecting cells under stress conditions. Here, we demonstrated the presence of Sirt3 in neurons, and characterized the role of Sirt3 in neuron survival under NMDA-induced excitotoxicity. METHODOLOGY/PRINCIPAL FINDINGS: To induce excitotoxic injury, we exposed primary cultured mouse cortical neurons to NMDA (30 µM). NMDA induced a rapid decrease of cytoplasmic NAD (but not mitochondrial NAD) in neurons through poly (ADP-ribose) polymerase-1 (PARP-1) activation. Mitochondrial Sirt3 was increased following PARP-1 mediated NAD depletion, which was reversed by either inhibition of PARP-1 or exogenous NAD. We found that massive reactive oxygen species (ROS) produced under this NAD depleted condition mediated the increase in mitochondrial Sirt3. By transfecting primary neurons with a Sirt3 overexpressing plasmid or Sirt3 siRNA, we showed that Sirt3 is required for neuroprotection against excitotoxicity. CONCLUSIONS: This study demonstrated for the first time that mitochondrial Sirt3 acts as a prosurvival factor playing an essential role to protect neurons under excitotoxic injury

Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins

Background: Members of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM).Result: The amino acid compositions of proteobacterial proteins were found to be different from those of plant proteins. We developed an SVM model based on amino acid and dipeptide compositions to distinguish between a proteobacterial protein and a plant protein. The amino acid composition (AAC) based SVM model had an accuracy of 92.44% with 0.85 Matthews correlation coefficient (MCC) while the dipeptide composition (DC) based SVM model had a maximum accuracy of 94.67% and 0.89 MCC. We also developed SVM models based on a hybrid approach (AAC and DC), which gave a maximum accuracy 94.86% and a 0.90 MCC. The models were tested on unseen or untrained datasets to assess their validity.Conclusion: The results indicate that the SVM based on the AAC and DC hybrid approach can be used to distinguish proteobacterial from plant protein sequences.Peer reviewedBiochemistry and Molecular Biolog