Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata).

BACKGROUND: Rice bean (Vigna umbellata), an underrated legume, adapts to diverse climatic conditions with the potential to support food and nutritional security worldwide. It is used as a vegetable, minor food crop and a fodder crop, being a rich source of proteins, minerals, and essential fatty acids. However, little effort has been made to decipher the genetic and molecular basis of various useful traits in this crop. Therefore, we considered three economically important traits i.e., flowering, maturity and seed weight of rice bean and identified the associated candidate genes employing an associative transcriptomics approach on 100 diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank. RESULTS: The transcriptomics-based genotyping of one-hundred diverse rice bean cultivars followed by pre-processing of genotypic data resulted in 49,271 filtered markers. The STRUCTURE, PCA and Neighbor-Joining clustering of 100 genotypes revealed three putative sub-populations. The marker-trait association analysis involving various genome-wide association study (GWAS) models revealed significant association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The transcript annotation provided information on the putative candidate genes for the considered traits. The candidate genes identified for flowering include HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A. Further, the WRKY1 and DEAD-box-RH27 were found to be associated with seed weight. Furthermore, the associations of PIF3 and pentatricopeptide-repeat-containing-gene with maturity and seed weight, and aldo-keto-reductase with flowering and maturity were revealed. CONCLUSION: This study offers insights into the genetic basis of key agronomic traits in rice bean, including flowering, maturity, and seed weight. The identified markers and associated candidate genes provide valuable resources for future exploration and targeted breeding, aiming to enhance the agronomic performance of rice bean cultivars. Notably, this research represents the first transcriptome-wide association study in pulse crop, uncovering the candidate genes for agronomically useful traits.

Finger millet (Eleusine coracana L.): from staple to superfood-a comprehensive review on nutritional, bioactive, industrial, and climate resilience potential.

MAIN CONCLUSION: This review discusses the Finger millet's rich nutritional profile, bioactive potential, and industrial applications, combined with its climate resilience, which make it a promising crop for enhancing food security and promoting sustainable agriculture. This review also highlights its significant potential to address malnutrition and mitigate climate change impacts. The emergence of Finger millet from "poor man's staple food" to "a nutrient rich cereal" has encouraged the need to explore this crop at a wider scale. It is a highly significant crop due to its rich nutritional and bioactive profile, diverse biological activities, and promising industrial applications, along with the high climate resilience. This comprehensive review evaluates its nutritional composition by comparing favorably with other cereals and millets and emphasizing its potential to address malnutrition and enhance food security. Furthermore, it explores the phytochemical/bioactive potential and strategies to enhance their bioavailability followed biological activities of Finger millet by highlighting its various health-promoting properties. The review also discusses industrial potential of finger millet including its role in nutraceutical and functional food production, as well as bioenergy generation. In addition, role of Finger millet as a climate-resilient crop; specifically, the available genetic resources and identification of genes and quantitative trait loci (QTLs) associated with major stress tolerance traits have also been discussed. By providing a comprehensive synthesis of existing knowledge, this study offers valuable insights for researchers, policymakers, and stakeholders engaged in efforts to promote sustainable agriculture, enhance food and nutrition security, and mitigate the impacts of climate change.

Multivariate Data Analysis Assisted Mining of Nutri-rich Genotypes from North Eastern Himalayan Germplasm Collection of Perilla (Perilla frutescens L.).

Understanding the nutritional diversity in Perilla (Perilla frutescens L.) is essential for selecting and developing superior varieties with enhanced nutritional profiles in the North Eastern Himalayan (NEH) region of India. In this study, we assessed the nutritional composition of 45 diverse perilla germplasm collected from five NEH states using standard protocols and advanced analytical techniques. Significant variability was observed in moisture (0.39-11.67%), ash (2.59-7.13%), oil (28.65-74.20%), protein (11.05-23.15%), total soluble sugars (0.34-3.67%), starch (0.01-0.55%), phenols (0.03-0.87%), ferric reducing antioxidant power (0.45-1.36%), palmitic acid (7.06-10.75%), stearic acid (1.96-2.29%), oleic acid (8.11-13.31%), linoleic acid (15.18-22.74%), and linolenic acid (55.47-67.07%). Similarly, significant variability in mineral content (ppm) was also observed for aluminium, calcium, cobalt, chromium, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorus, and zinc. Multivariate analyses, including hierarchical clustering analysis (HCA) and principal component analysis (PCA), revealed the enriched nutritional diversity within the germplasm. Correlation analysis indicated significant positive and negative relationships between nutritional parameters, indicating potential biochemical and metabolic interactions present in the perilla seeds. TOPSIS-based ranking identified promising genotypes for functional foods, pharmaceuticals, and nutritional applications. This study provides a first in-depth report of the nutritional composition and diversity of perilla germplasm in the NEH region, thus aiding in the identification of superior varieties for food and nutritional diversification and security.

Evaluation of Chemical Composition among the Multi Colored Germplasm of Abrus precatorius L.

The medicinal plant Abrus precatorius L. was traditionally used in the Siddha and Ayurvedic systems of medicine in India. The Indian center of origin holds a vast variability in its seed color. The objective of this study was to assess the total monomeric anthocyanin, flavonol, as well as the antioxidative potential, protein content and ash content among the accessions. A total of 99 accessions conserved in the Indian National Genebank were used in this study. The methods used for the estimation of total monomeric anthocyanin, flavonol, as well as the antioxidative potential, protein content and ash content were the pH differential method, Oomah method, Ferric Reducing Antioxidant Potential, Dumas method and gravimetric method, respectively. The completely black colored accession was recorded with highest total monomeric anthocyanin (51.95 mg/100 g of cyanidin 3-glucoside equivalent) and flavonol content (66.41 mg/g of quercetin equivalent). Red + black colored accessions have recorded the maximum value with respect to antioxidants (14.18 mg/g of gallic acid equivalent). The highest amount of protein content was found in a completely white colored accession (20.67%) and the maximum ash content was recorded in red + black colored accession (4.01%). The promising accessions identified can be used by pharmaceutical companies in drug development and in curing degenerative diseases.

Understanding natural genetic variation for nutritional quality in grain and identification of superior haplotypes in deepwater rice genotypes of Assam, India.

Rice grown under deepwater ecosystem is considered to be natural farming and hence they are considered to be input efficient. Thus, to identify gene responsible for nutritional content under natural conditions, a genome-wide association study (GWAS)was performed. GWAS identified single nucleotide polymorphisms (SNPs) significantly associated with various nutritional quality traits such as Zn (mg/kg), Fe (mg/kg), Protein (%), Oil (%), Amylose (%), Starch (%), Phytic acid (%), Phenol (%) and TDF (%) in 184 deepwater rice accessions evaluated over 2 consecutive years. A total of 278 SNPs distributed across 12 chromosomes were found to be significantly associated with Zn, Oil and Phenol content. Among them, eight high confidence SNPs were significant and identified on chr1 (AX-95933712), chr7 (AX-95957036), and chr8 (AX-95965181) for Zn content. Similarly, on chr2 (AX-95945186), chr8 (AX-95964718), and chr11 (AX-95961099) have been found to be associated with Oil content and on chr3 (AX-95922121) and chr4 (AX-95963889) for Phenol content. Genomic regions of ± 220 kb flanking the three consistent lowest p value containing SNPs for each trait were considered for finding superior haplotypes. These SNPs showed significant phenotypic variations with different identified haplotype blocks. The allelic variations with phenotypes were considered to be superior haplotypes i.e., Block 1: Hap 1 (ACCC) for high Zn content, Block 2: Hap 1 (CT) for high Oil content, and Block 2: Hap 1(CGGG) for low Phenol content. The discovered superior haplotype with high nutritional content could be important for understanding the mechanisms involving nutrient use efficiency. Thus, the present study demonstrated that developing rice varieties with appropriate nutritional quality traits will be possible through the incorporation of such superior haplotypes in breeding programs.

Genome-Wide Comparative Analysis of Five Amaranthaceae Species Reveals a Large Amount of Repeat Content.

Amaranthus is a genus of C4 dicotyledonous herbaceous plant species that are widely distributed in Asia, Africa, Australia, and Europe and are used as grain, vegetables, forages, and ornamental plants. Amaranth species have gained significant attention nowadays as potential sources of nutritious food and industrial products. In this study, we performed a comparative genome analysis of five amaranth species, namely, Amaranthus hypochondriacus, Amaranthus tuberculatus, Amaranthus hybridus, Amaranthus palmeri, and Amaranthus cruentus. The estimated repeat content ranged from 54.49% to 63.26% and was not correlated with the genome sizes. Out of the predicted repeat classes, the majority of repetitive sequences were Long Terminal Repeat (LTR) elements, which account for about 13.91% to 24.89% of all amaranth genomes. Phylogenetic analysis based on 406 single-copy orthologous genes revealed that A. hypochondriacus is most closely linked to A. hybridus and distantly related to A. cruentus. However, dioecious amaranth species, such as A. tuberculatus and A. palmeri, which belong to the subgenera Amaranthus Acnida, have formed their distinct clade. The comparative analysis of genomic data of amaranth species will be useful to identify and characterize agronomically important genes and their mechanisms of action. This will facilitate genomics-based, evolutionary studies, and breeding strategies to design faster, more precise, and predictable crop improvement programs.

A comprehensive review on nutritional, nutraceutical, and industrial perspectives of perilla (Perilla frutscens L.) seeds - An orphan oilseed crop.

There is a growing need to mainstream orphan or underutilized crops to enhance nutritional security and sustainable agriculture. Among these, Perilla frutescens L. is an important crop due to its rich nutritional and phytochemical content which makes it significant in nutrition, medicine, and industrial sector. Perilla seeds are mainly rich in ω-3 fatty acids, dietary fiber, amino acids, vitamins, and minerals, high α-linolenic acid, which contributes to their health benefits. This review explores the nutritional profile of perilla seeds and highlights its unique composition compared to other oilseed crops. It also analyzes the phytochemical components of perilla seeds and their various biological activities, including antioxidant, antidiabetic, antiobesity, cardioprotective, anticancer, antimicrobial, neuroprotective, and anti-inflammatory effects. These activities demonstrate the potential of perilla seeds in both pharmaceutical and food sectors. The review also covers recent advancements in genomics and transgenic research discussing potential areas for crop improvement. Additionally, it explores the use of perilla seeds in functional foods, blending perilla oil with other oils, and their applications in enhancing product formulations. This review offers valuable insights for researchers, students, policymakers, environmentalists, and industry professionals by detailing the potential of perilla seeds across various sectors. The findings support sustainable agriculture, crop diversification, and innovative product development, thus contributing to the integration of perilla into mainstream agriculture.

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.