Computational gene expression profiling under salt stress reveals patterns of co-expression

Genome-wide analysis of wall associated kinase (WAK) gene family in barley

In plants, wall associated kinases (WAKs) form a unique subfamily of receptor like-kinases (RLKs). In Arabidopsis thaliana, WAK-RLKs are known to regulate biotic stress, cell expansion, and metal tolerance, but their detailed characterization in barley is lacking. In this study, we identified a total of 91 WAK genes in the barley genome and classified them into five groups. Evolutionary analysis of HvWAKs with AtWAKs revealed their species-specific expansion. The maximum number (19 to 20) of WAK genes were located on chromosomes 3, 5 and 6. WAK proteins exhibited similar types of motif distribution in their group. Characterization of a Ds transposon insertion mutant of the wak1 revealed differences in the root length. Further, HvSPL23 transcription factor was identified as a positive co-expressing gene with HvWAK1, suggesting its possible upstream regulator. Taken together, our study provides a base for the functional characterization of WAK family members in the future.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Transcriptomic Analysis of Soil Grown T. aestivum cv. Root to Reveal the Changes in Expression of Genes in Response to Multiple Nutrients Deficiency

Deficiency of necessary macronutrients, i.e., Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), and Sulfate (S) in the soil leads to a reduction in plant growth and yield, which is a result of changes in expression level of various genes. This study was performed to identify the differentially expressed genes and its associated metabolic pathways occurred in soil grown wheat root samples excavated from the control and treated fields. To identify the difference in gene expression levels due to deficiency of the said nutrients, a transcriptomic, meta-analysis was performed on array expression profile data. A set of 435 statistically significant probes encoding 398 Nutrient Deficiency Response Genes (NRGs) responding at-least one nutrients deficiency (ND) were identified. Out of them 55 NRGs were found to response to minimum two ND. Singular Enrichment Analysis (SEA) predicts ontological based classifications and functional analysis of NRGs in different cellular/molecular pathways involved in root development and growth. Functional annotation and reaction mechanism of differentially expressed genes, proteins/enzymes in the different metabolic pathway through MapMan analysis were explored. Further the meta-analysis was performed to revels the active involvement each NRGs in distinct tissues and their comparative potential expression analysis in different stress conditions. The study results in exploring the role of major acting candidate genes such as Non-specific serine/threonine protein kinase, Xyloglucan endotransglucosylase/hydrolase, Peroxides, Glycerophosphoryl diester phosphodiesterase, S-adenosylmethionine decarboxylase proenzyme, Dehydrin family proteins, Transcription factors, Membrane Proteins, Metal binding proteins, Photosystem proteins, Transporter and Transferase associated in different metabolic pathways. Finally, the differences of transcriptional responses in the soil-grown root of T. aestivum cv. and in-vitro grown model plants under nutrients deficiency were summarized.