Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops.

Vegetable crops possess a prominent nutri-metabolite pool that not only contributes to the crop performance in the fields, but also offers nutritional security for humans. In the pursuit of identifying, quantifying and functionally characterizing the cellular metabolome pool, biomolecule separation technologies, data acquisition platforms, chemical libraries, bioinformatics tools, databases and visualization techniques have come to play significant role. High-throughput metabolomics unravels structurally diverse nutrition-rich metabolites and their entangled interactions in vegetable plants. It has helped to link identified phytometabolites with unique phenotypic traits, nutri-functional characters, defense mechanisms and crop productivity. In this study, we explore mining diverse metabolites, localizing cellular metabolic pathways, classifying functional biomolecules and establishing linkages between metabolic fluxes and genomic regulations, using comprehensive metabolomics deciphers of the plant's performance in the environment. We discuss exemplary reports covering the implications of metabolomics, addressing metabolic changes in vegetable plants during crop domestication, stage-dependent growth, fruit development, nutri-metabolic capabilities, climatic impacts, plant-microbe-pest interactions and anthropogenic activities. Efforts leading to identify biomarker metabolites, candidate proteins and the genes responsible for plant health, defense mechanisms and nutri-rich crop produce are documented. With the insights on metabolite-QTL (mQTL) driven genetic architecture, molecular breeding in vegetable crops can be revolutionized for developing better nutritional capabilities, improved tolerance against diseases/pests and enhanced climate resilience in plants.

Core microbiota of wheat rhizosphere under Upper Indo-Gangetic plains and their response to soil physicochemical properties.

Wheat is widely cultivated in the Indo-Gangetic plains of India and forms the major staple food in the region. Understanding microbial community structure in wheat rhizosphere along the Indo-Gangetic plain and their association with soil properties can be an important base for developing strategies for microbial formulations. In the present study, an attempt was made to identify the core microbiota of wheat rhizosphere through a culture-independent approach. Rhizospheric soil samples were collected from 20 different sites along the upper Indo-Gangetic plains and their bacterial community composition was analyzed based on sequencing of the V3-V4 region of the 16S rRNA gene. Diversity analysis has shown significant variation in bacterial diversity among the sites. The taxonomic profile identified Proteobacteria, Chloroflexi, Actinobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Planctomycetes, Verrucomicrobia, Firmicutes, and Cyanobacteria as the most dominant phyla in the wheat rhizosphere in the region. Core microbiota analysis revealed 188 taxa as core microbiota of wheat rhizosphere with eight genera recording more than 0.5% relative abundance. The order of most abundant genera in the core microbiota is Roseiflexus> Flavobacterium> Gemmatimonas> Haliangium> Iamia> Flavisolibacter> Ohtaekwangia> Herpetosiphon. Flavobacterium, Thermomonas, Massilia, Unclassified Rhizobiaceae, and Unclassified Crenarchaeota were identified as keystone taxa of the wheat rhizosphere. Correlation studies revealed, pH, organic carbon content, and contents of available nitrogen, phosphorus, and iron as the major factors driving bacterial diversity in the wheat rhizosphere. Redundancy analysis has shown the impact of different soil properties on the relative abundance of different genera of the core microbiota. The results of the present study can be used as a prelude to be developing microbial formulations based on core microbiota.

CerealESTDb: A Comprehensive Resource for Abiotic Stress-Responsive Annotated ESTs With Predicted Genes, Gene Ontology, and Metabolic Pathways in Major Cereal Crops.

Cereals are the most important food crops and are considered key contributors to global food security. Loss due to abiotic stresses in cereal crops is limiting potential productivity in a significant manner. The primary reasons for abiotic stresses are abrupt temperature, variable rainfall, and declining nutrient status of the soil. Varietal development is the key to sustaining productivity under influence of multiple abiotic stresses and must be studied in context with genomics and molecular breeding. Recently, advances in a plethora of Next Generation Sequencing (NGS) based methods have accelerated the enormous genomic data generation associated with stress-induced transcripts such as microarray, RNAseq, Expressed Sequenced Tag (ESTs), etc. Many databases related to microarray and RNA-seq based transcripts have been developed and profusely utilized. However, an abundant amount of transcripts related to abiotic stresses in various cereal crops arising from EST technology are available but still remain underutilized in absence of a consolidated database. In this study, an attempt has been made with a primary goal to integrate, analyse, and characterise the available resources of ESTs responsive to abiotic stresses in major cereals. The developed CerealESTdb presents a customisable search in two different ways in the form of searchable content for easy access and potential use. This database comprises ESTs from four major cereal crops, namely rice (Oryza sativa L.), wheat (Triticum aestivum L.), sorghum (Sorghum bicolour L.), and maize (Zea mays L.), under a set of abiotic stresses. The current statistics of this cohesive database consists of 55,826 assembled EST sequences, 51,791 predicted genes models, and their 254,609 gene ontology terms including extensive information on 1,746 associated metabolic pathways. We anticipate that developed CerealESTdb will be helpful in deciphering the knowledge of complex biological phenomena under abiotic stresses to accelerate the molecular breeding programs towards the development of crop cultivars resilient to abiotic stresses. The CerealESTdb is publically available with the URL http://cabgrid.res.in/CerealESTDb.

Prediction of miRNA and Identification of their Relationship Network Related to Late Blight Disease of Potato.

BACKGROUND: Late blight is a serious disease in potato caused by Phytophthora infestans. To date only few miRNA have been discovered which are related to late blight disease of potato during host pathogen interaction. Recent studies showed that miRNA, an important gene expression regulator, plays a very important role in host-pathogen interaction by silencing genes either by destructing or blocking of translation of mRNA. METHOD: Homology search was performed between non-redundant mature miRNA sequences from miRBase database and Solanum tuberosum EST sequences from NCBI database. Screening of the potential miRNA was done after secondary structure prediction. The target related to late blight disease of respective miRNA was functionally annotated. To identify the relationship between the predicted and mature miRNAs, multiple sequence alignment and evolutionary relationships were established. RESULTS AND CONCLUSION: 34 Candidate miRNA related to late blight disease of potato were identified which were associated to five target genes. These miRNAs were linked with Avr3a, INF1, INF2b genes which are elicitin like protein and triggers a hypersensitive response to host cell. Mapping of target sequences showed similarity with Solanum lycopersicum NRC1 gene of chr.1, which are reported as a casual protein required for Pto-mediated cell death and resistance in N. benthamiana. NRC1 are considered as a RX-CC_like domain-containing protein which shows similarity with coiledcoil domain of the potato virus X resistance protein (RX) in Solanum tuberosum. RX recognizes pathogen effector proteins and triggers a response that may be as severe as localized cell death thereby providing resistance against potato virus X.

The Halophile protein database.

Halophilic archaea/bacteria adapt to different salt concentration, namely extreme, moderate and low. These type of adaptations may occur as a result of modification of protein structure and other changes in different cell organelles. Thus proteins may play an important role in the adaptation of halophilic archaea/bacteria to saline conditions. The Halophile protein database (HProtDB) is a systematic attempt to document the biochemical and biophysical properties of proteins from halophilic archaea/bacteria which may be involved in adaptation of these organisms to saline conditions. In this database, various physicochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (Gravy) have been listed. These physicochemical properties play an important role in identifying the protein structure, bonding pattern and function of the specific proteins. This database is comprehensive, manually curated, non-redundant catalogue of proteins. The database currently contains 59 897 proteins properties extracted from 21 different strains of halophilic archaea/bacteria. The database can be accessed through link. Database URL: http://webapp.cabgrid.res.in/protein/

Trends in the codon usage patterns of Chromohalobacter salexigens genes.

Chromohalobacter salexigens, a Gammaproteobacterium belonging to the family Halomonadaceae, shows a broad salinity range for growth. In order to reveal the factors influencing architecture of protein coding genes in C. salexigens, pattern of synonymous codon usage bias has been investigated. Overall codon usage analysis of the microorganism revealed that C and G ending codons are predominantly used in all the genes which are indicative of mutational bias. Multivariate statistical analysis showed that the genes are separated along the first major explanatory axis according to their expression levels and their genomic GC content at the synonymous third positions of the codons. Both NC plot and correspondence analysis on Relative Synonymous Codon Usage (RSCU) indicates that the variation in codon usage among the genes may be due to mutational bias at the DNA level and natural selection acting at the level of mRNA translation. Gene length and the hydrophobicity of the encoded protein also influence the codon usage variation of genes to some extent. A comparison of the relative synonymous codon usage between 10% each of highly and lowly expressed genes determines 23 optimal codons, which are statistically over represented in the former group of genes and may provide useful information for salt-stressed gene prediction and gene-transformation. Furthermore, genes for regulatory functions; mobile and extrachromosomal element functions; and cell envelope are observed to be highly expressed. The study could provide insight into the gene expression response of halophilic bacteria and facilitate establishment of effective strategies to develop salt-tolerant crops of agronomic value.