Plant metabolomics is a rapidly advancing field of plant sciences and systems biology. It involves comprehensive analyses of small molecules (metabolites) in plant tissues and cells. These metabolites include a wide range of compounds, such as sugars, amino acids, organic acids, secondary metabolites (e.g., alkaloids and flavonoids), lipids, and more. Metabolomics allows an understanding of the functional roles of specific metabolites in plants’ physiology, development, and responses to biotic and abiotic stresses. It can lead to the identification of metabolites linked with specific traits or functions. Plant metabolic networks and pathways can be better understood with the help of metabolomics. Researchers can determine how plants react to environmental cues or genetic modifications by examining how metabolite profiles change under various crop stages. Metabolomics plays a major role in crop improvement and biotechnology. Integrating metabolomics data with other omics data (genomics, transcriptomics, and proteomics) provides a more comprehensive perspective of plant biology. This systems biology approach enables researchers to understand the complex interactions within organisms.

Plant growth-promoting rhizobacteria (PGPR) are a group of root-associated beneficial bacteria emerging as one of the powerful agents in sustainable plant disease management. Among the PGPR, Bacillus sp. has become a popular biocontrol agent for controlling pests and the diseases of several crops of agricultural and horticultural importance. Understanding the molecular basis of the plant growth-promoting and biocontrol abilities of Bacillus spp. will allow us to develop multifunctional microbial consortia for sustainable agriculture. In our study, we attempted to unravel the genome complexity of the potential biocontrol agent Bacillus subtilis Bbv57 (isolated from the betelvine’s rhizosphere), available at TNAU, Coimbatore. A WGS analysis generated 26 million reads, and a de novo assembly resulted in the generation of 4,302,465 bp genome of Bacillus subtilis Bbv57 containing 4363 coding sequences (CDS), of which 4281 were functionally annotated. An analysis of 16S rRNA revealed its 100% identity to Bacillus subtilis IAM 12118. A detailed data analysis identified the presence of >100 CAZymes and nine gene clusters involved in the production of secondary metabolites that exhibited antimicrobial properties. Further, Bbv57 was found to harbor 282 unique genes in comparison with 19 other Bacillus strains, requiring further exploration.

Stomata regulates conductance, transpiration and photosynthetic traits in plants. Increased stomatal density may contribute to enhanced water loss and thereby help improve the transpirational cooling process and mitigate the high temperature-induced yield losses. However, genetic manipulation of stomatal traits through conventional breeding still remains a challenge due to problems involved in phenotyping and the lack of suitable genetic materials. Recent advances in functional genomics in rice identified major effect genes determining stomatal traits, including its number and size. Widespread applications of CRISPR/Cas9 in creating targeted mutations paved the way for fine tuning the stomatal traits for enhancing climate resilience in crops. In the current study, attempts were made to create novel alleles of OsEPF1 (Epidermal Patterning Factor), a negative regulator of stomatal frequency/density in a popular rice variety, ASD 16, using the CRISPR/Cas9 approach. Evaluation of 17 T₀ progenies identified varying mutations (seven multiallelic, seven biallelic and three monoallelic mutations). T₀ mutant lines showed a 3.7–44.3% increase in the stomatal density, and all the mutations were successfully inherited into the T₁ generation. Evaluation of T₁ progenies through sequencing identified three homozygous mutants for one bp insertion. Overall, T₁ plants showed 54–95% increased stomatal density. The homozygous T₁ lines (# E1-1-4, # E1-1-9 and # E1-1-11) showed significant increase in the stomatal conductance (60–65%), photosynthetic rate (14–31%) and the transpiration rate (58–62%) compared to the nontransgenic ASD 16. Results demonstrated that the genetic alterations in OsEPF1 altered the stomatal density, stomatal conductance and photosynthetic efficiency in rice. Further experiments are needed to associate this technology with canopy cooling and high temperature tolerance.

Rice (Oryza sativa L.) is the staple food of the majority of the population, particularly in Asia and Africa. Enriching rice with nutritional and therapeutic contents can improve its benefits for patients with lifestyle disorders. This study aimed to profile the phytochemical contents of the therapeutically known traditional rice Mappillai Samba against white rice CBMAS 14065 using non-targeted gas chromatography–mass spectrometry (GC-MS/MS). An analysis of the data using a mass spectrometry–data independent analysis (MS-DIAL) and MetaboAnalyst identified 113 metabolites belonging to 21 different classes of metabolites. A partial least square-discriminant analysis (PLS-DA) revealed 43 variable importance in projection (VIP) metabolites. This study identified therapeutically important metabolites, including phenylpropanoids, phytosterols, flavonoids, and polyamines, in the grains of Mappillai Samba. Three significant metabolic pathways, viz., phenylpropanoid biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, and steroid biosynthesis, were responsible for the grain metabolome variation between CBMAS 14065 and Mappillai Samba. Overall, the results of this study unravelled the biochemical complexity of Mappillai Samba, paving the way for the genetic mapping of the therapeutic compound accumulation in rice and the development of similar therapeutic rice varieties through molecular breeding.

Yellow mosaic disease (YMD), incited by mungbean yellow mosaic virus (MYMV), is a primary viral disease that reduces mungbean production in South Asia, especially in India. There is no detailed knowledge regarding the genes and molecular mechanisms conferring resistance of mungbean to MYMV. Therefore, disclosing the genetic and molecular bases related to MYMV resistance helps to develop the mungbean genotypes with MYMV resistance. In this study, transcriptomes of mungbean genotypes, VGGRU-1 (resistant) and VRM (Gg) 1 (susceptible) infected with MYMV were compared to those of uninfected controls. The number of differentially expressed genes (DEGs) in the resistant and susceptible genotypes was 896 and 506, respectively. Among them, 275 DEGs were common between the resistant and susceptible genotypes. Functional annotation of DEGs revealed that the DEGs belonged to the following categories defense and pathogenesis, receptor-like kinases; serine/threonine protein kinases, hormone signaling, transcription factors, and chaperons, and secondary metabolites. Further, we have confirmed the expression pattern of several DEGs by quantitative real-time PCR (qRT-PCR) analysis. Collectively, the information obtained in this study unveils the new insights into characterizing the MYMV resistance and paved the way for breeding MYMV resistant mungbean in the future.

Yams are economic and medicinal crops with a long growth cycle, spanning between 9–11 months due to their prolonged tuber dormancy. Tuber dormancy has constituted a major constraint in yam production and genetic improvement. In this study, we performed non-targeted comparative metabolomic profiling of tubers of two white yam genotypes, (Obiaoturugo and TDr1100873), to identify metabolites and associated pathways that regulate yam tuber dormancy using gas chromatography–mass spectrometry (GC–MS). Yam tubers were sampled between 42 days after physiological maturity (DAPM) till tuber sprouting. The sampling points include 42-DAPM, 56-DAPM, 87DAPM, 101-DAPM, 115-DAPM, and 143-DAPM. A total of 949 metabolites were annotated, 559 in TDr1100873 and 390 in Obiaoturugo. A total of 39 differentially accumulated metabolites (DAMs) were identified across the studied tuber dormancy stages in the two genotypes. A total of 27 DAMs were conserved between the two genotypes, whereas 5 DAMs were unique in the tubers of TDr1100873 and 7 DAMs were in the tubers of Obiaoturugo. The differentially accumulated metabolites (DAMs) spread across 14 major functional chemical groups. Amines and biogenic polyamines, amino acids and derivatives, alcohols, flavonoids, alkaloids, phenols, esters, coumarins, and phytohormone positively regulated yam tuber dormancy induction and maintenance, whereas fatty acids, lipids, nucleotides, carboxylic acids, sugars, terpenoids, benzoquinones, and benzene derivatives positively regulated dormancy breaking and sprouting in tubers of both yam genotypes. Metabolite set enrichment analysis (MSEA) revealed that 12 metabolisms were significantly enriched during yam tuber dormancy stages. Metabolic pathway topology analysis further revealed that six metabolic pathways (linoleic acid metabolic pathway, phenylalanine metabolic pathway, galactose metabolic pathway, starch and sucrose metabolic pathway, alanine-aspartate-glutamine metabolic pathways, and purine metabolic pathway) exerted significant impact on yam tuber dormancy regulation. This result provides vital insights into molecular mechanisms regulating yam tuber dormancy.