Genome-wide association studies for earliness, MYMIV resistance, and other associated traits in mungbean (Vigna radiata L. Wilczek) using genotyping by sequencing approach.

Yellow mosaic disease (YMD) remains a major constraint in mungbean (Vigna radiata (L.)) production; while short-duration genotypes offer multiple crop cycles per year and help in escaping terminal heat stress, especially during summer cultivation. A comprehensive genotyping by sequencing (GBS)-based genome-wide association studies (GWAS) analysis was conducted using 132 diverse mungbean genotypes for traits like flowering time, YMD resistance, soil plant analysis development (SPAD) value, trichome density, and leaf area. The frequency distribution revealed a wide range of values for all the traits. GBS studies identified 31,953 high-quality single nucleotide polymorphism (SNPs) across all 11 mungbean chromosomes and were used for GWAS. Structure analysis revealed the presence of two genetically distinct populations based on ΔK. The linkage disequilibrium (LD) varied throughout the chromosomes and at r2 = 0.2, the mean LD decay was estimated as 39.59 kb. Two statistical models, mixed linear model (MLM) and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) identified 44 shared SNPs linked with various candidate genes. Notable candidate genes identified include FPA for flowering time (VRADI10G01470; chr. 10), TIR-NBS-LRR for mungbean yellow mosaic India virus (MYMIV) resistance (VRADI09G06940; chr. 9), E3 ubiquitin-protein ligase RIE1 for SPAD value (VRADI07G28100; chr. 11), WRKY family transcription factor for leaf area (VRADI03G06560; chr. 3), and LOB domain-containing protein 21 for trichomes (VRADI06G04290; chr. 6). In-silico validation of candidate genes was done through digital gene expression analysis using Arabidopsis orthologous (compared with Vigna radiata genome). The findings provided valuable insight for marker-assisted breeding aiming for the development of YMD-resistant and early-maturing mungbean varieties.

Bioactivity assessment of essential oils of Cymbopogon species using a network pharmacology approach.

Essential oils of Cymbopogon species have wide commercial applications in fragrance, perfumery, and pharmaceuticals as they exhibit a horizon of bioactivities. Here, essential oils of C. flexuosus and C. martinii were analysed to identify bioactive constituents and bioactivities using a network pharmacology approach. Essential oils were isolated using hydro-distillation in a mini Clevenger apparatus. Analysis of essential oils by GC-MS revealed 20 and 15 chemical constituents in C. flexuosus and C. martinii, respectively. An ingredient-target protein-pathway network was constructed comprising 10 oil constituents (citral, geraniol, geranyl acetate, limonene, linalool, α-terpineol, borneol, α-pinene, myrcene, and n-decanol), 14 target proteins, 51 related pathways, and 108 connections. Analyses of the network showed geraniol, geranyl acetate, limonene, linalool, and citral as major active constituents. A core sub-network constructed from the ingredient-target protein-pathway network revealed bioactivities including anti-cancer, anti-inflammatory and neuroprotective. The protein association network pointed out the major target proteins viz., THRB, FXR, ALOX15, and TSHR and pathways like metabolic, and neuroactive ligand-receptor interaction pathways of essential oil constituents. The target proteins and pathways provided insights into the mechanism of action of bioactive constituents. Based on the results of the study, geraniol was correlated with neuroprotective, citral to chemo-preventive, and limonene to anti-inflammatory activities. Thus, the study offers a new way for the assessment of the bioactivities of Cymbopogon species essential oils leading to the development of new biomedicines.

Deciphering functional biomolecule potential of marine diatoms through complex network approach.

Marine diatoms are unique reservoirs of bioactive compounds having enormous applications in therapeutics. But high-throughput screening methods are needed to elucidate the interaction between numerous biomolecules and their targets, facilitating rapid screening for novel drug molecules. So, in the present study chemical constituents were extracted from five marine diatoms using un-targeted metabolite profiling and in-silico virtual screening bioinformatics was employed to predict their bioactivity and molecular targets. A total of 17 chemical constituents out of 51 showed interactions with 76 protein targets associated with 213 pathways. Ingredient-target-pathway network revealed oleic acid, linoleic acid and cholest-5-en-3-ol as major active constituents. Core subnetwork and protein association network showed involvement of these compounds in key metabolic pathways related to cell signaling, cell growth and metabolism of xenobiotics. Thus, the present study for the first time revealed the main active ingredients and their associated pathways from marine diatoms using complex network approach.

New paradigm in diatom omics and genetic manipulation.

Diatoms are one of the most heterogeneous eukaryotic plankton known for regulating earth's biogeochemical cycles and maintaining the marine ecosystems ever since the late Eocene epoch. The advent of multidisciplinary omics approach has both epitomized and revolutionized the nature of their chimeric genetic toolkit, ecophysiology, and metabolic adaptability as well as their interaction with other communities. In addition, advanced functional annotation of transcriptomic and proteomic data using cutting edge bioinformatics tools together with high-resolution genome-scale mathematical modeling has effectively proven as the catapult in solving genetic bottlenecks in microbial as well as diatom exploration. In this review, a corroborative summation of the robust work done in manipulating, engineering, and sequencing of the diatom genomes besides underpinning the holistic application of omics in transcription and translation has been discussed in order to shrewd their multifarious novel potential in the field of biotechnology and provide an insight into their dynamic evolutionary relevance.

Computational Identification of Essential Enzymes as Potential Drug Targets in Shigella flexneri Pathogenesis Using Metabolic Pathway Analysis and Epitope Mapping.

Shigella flexneri is a facultative intracellular pathogen that causes bacillary dysentery in humans. Infection with S. flexneri can result in more than a million deaths yearly and most of the victims are children in developing countries. Therefore, identifying novel and unique drug targets against this pathogen is instrumental to overcome the problem of drug resistance to the antibiotics given to patients as the current therapy. In this study, a comparative analysis of the metabolic pathways of the host and pathogen was performed to identify this pathogen's essential enzymes for the survival and propose potential drug targets. First, we extracted the metabolic pathways of the host, Homo sapiens, and pathogen, S. flexneri, from the KEGG database. Next, we manually compared the pathways to categorize those that were exclusive to the pathogen. Further, all enzymes for the 26 unique pathways were extracted and submitted to the Geptop tool to identify essential enzymes for further screening in determining the feasibility of the therapeutic targets that were predicted and analyzed using PPI network analysis, subcellular localization, druggability testing, gene ontology and epitope mapping. Using these various criteria, we narrowed it down to prioritize 5 novel drug targets against S. flexneri and one vaccine drug targets against all strains of Shigella. Hence, we suggest the identified enzymes as the best putative drug targets for the effective treatment of S. flexneri.