Diversity in KCS2 (Ketoacyl-CoA Synthase) of selected plants and its molecular implications: A computational analysis

The majority of calories in human food are derived from plant fatty acids. Besides,plant fatty acids are also a major component of a variety of products useful to human beings such as paints, cosmetics, biofuels, lubricants, detergents and soaps. Ketoacyl-CoA synthase is a key enzyme involved in the fatty acid elongation in plants In this study, we have analyzed the diversity in the KCS2 proteins of a selected plant species. We conclude that though there are extensive similarities in the KCS2 proteins studied with respect to total number of negatively charged residues, total number of positively charged residues, and domain organization, there are notable differences for other features such as extinction coefficients, protein stability, kinase specific phosphorylation sites, number of O-GlcNAc sites, predicted sumoylation sites, residues contributing to nuclear export signal and transmemebrane helices. These differences may have repercussions for the quantitative efficiency of the 3-Ketoacyl-CoA synthase enzyme which catalyzes the condensation of c2 units to acyl coA during the fatty acid elongation process, and its regulation. This paper showcases molecular implications of diversity in KCS2 , which can be used to create a diverse genetic base for engineering KCS 2 genes

A computational study on genetic diversity of shatterproof1 (shp1) and shatterproof2 (shp2) genes in some members of Oleraceae and its molecular implications

Abstract Dispersal and maturation of seed is a complex event in flowering plants. The genes shatterproof1 (shp1) and shatterproof2 (shp2) are essential for fruit dehiscence in Arabidopsis. In this study, we have analyzed the diversity in these two genes and their molecular implications in some members of Oleraceae. We have studied the gene organization of these two genes and various biochemical and biophysical parameters of the proteins encoded by these two genes. Though there are some similarities, there also exist some notable differences. These differences could be exploited for creating a library of synthetic alleles (neutral or advantageous) to be used for genetic engineering, thus ensuring a wide genetic base. This diversity analysis may be significant to create diversity in the transgenic plants for shattering resistance using genetic engineered methods. This analysis explores the possible correlation of results of this study with the phenotypic data to derive functional significance of the diversity in SHP genes

IMAGE-BASED IDENTIFICATION OF MLB DISEASE OF MAIZE

Not AvailableIn recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.) crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease) have been collected from different agricultural farms using hand-held camera and smartphones. The images have been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018–19. The architectural framework of popular state-of-the network ‘GoogleNet’ has been used to build the deep CNN model. The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained model has achieved an overall accuracy of 99.14% on the separate test dataset.Not Availabl

Genome-wide identification and characterization of Puccinia striiformis-responsive lncRNAs in Triticum aestivum

Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici (Pst) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines—rust resistance line FLW29 and rust susceptible line PBW343—which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst-resistant and Pst-susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease