Nutritional Composition and Bioactive Components in Quinoa (Chenopodium quinoa Willd.) Greens: A Review.

Quinoa (Chenopodium quinoa Willd.) is a nutrient-rich grain native to South America and eaten worldwide as a healthy food, sometimes even referred to as a "superfood". Like quinoa grains, quinoa greens (green leaves, sprouts, and microgreens) are also rich in nutrients and have health promoting properties such as being antimicrobial, anticancer, antidiabetic, antioxidant, antiobesity, and cardio-beneficial. Quinoa greens are gluten-free and provide an excellent source of protein, amino acids, essential minerals, and omega-3 fatty acids. Quinoa greens represent a promising value-added vegetable that could resolve malnutrition problems and contribute to food and nutritional security. The greens can be grown year-round (in the field, high tunnel, and greenhouse) and have short growth durations. In addition, quinoa is salt-, drought-, and cold-tolerant and requires little fertilizer and water to grow. Nevertheless, consumption of quinoa greens as leafy vegetables is uncommon. To date, only a few researchers have investigated the nutritional properties, phytochemical composition, and human health benefits of quinoa greens. We undertook a comprehensive review of the literature on quinoa greens to explore their nutritional and functional significance to human health and to bring awareness to their use in human diets.

Evaluation of high yielding soybean germplasm under water limitation.

Limited information is available for soybean root traits and their plasticity under drought stress. To date, no studies have focused on examining diverse soybean germplasm for regulation of shoot and root response under water limited conditions across varying soil types. In this study, 17 genetically diverse soybean germplasm lines were selected to study root response to water limited conditions in clay (trial 1) and sandy soil (trial 2) in two target environments. Physiological data on shoot traits was measured at multiple crop stages ranging from early vegetative to pod filling. The phenotypic root traits, and biomass accumulation data are collected at pod filling stage. In trial 1, the number of lateral roots and forks were positively correlated with plot yield under water limitation and in trial 2, lateral root thickness was positively correlated with the hill plot yield. Plant Introduction (PI) 578477A and 088444 were found to have higher later root number and forks in clay soil with higher yield under water limitation. In sandy soil, PI458020 was found to have a thicker lateral root system and higher yield under water limitation. The genotypes identified in this study could be used to enhance drought tolerance of elite soybean cultivars through improved root traits specific to target environments.

Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing.

The objective of this study was to use next-generation sequencing technologies to dissect quantitative trait loci (QTL) for southern root-knot nematode (RKN) resistance into individual genes in soybean. Two hundred forty-six recombinant inbred lines (RIL) derived from a cross between Magellan (susceptible) and PI 438489B (resistant) were evaluated for RKN resistance in a greenhouse and sequenced at an average of 0.19× depth. A sequence analysis pipeline was developed to identify and validate single-nucleotide polymorphisms (SNPs), infer the parental source of each SNP allele, and genotype the RIL population. Based on 109,273 phased SNPs, recombination events in RILs were identified, and a total of 3,509 bins and 3,489 recombination intervals were defined. About 50.8% of bins contain 1 to 10 genes. A linkage map was subsequently constructed by using bins as molecular markers. Three QTL for RKN resistance were identified. Of these, one major QTL was mapped to bin 10 of chromosome 10, which is 29.7 kb in size and harbors three true genes and two pseudogenes. Based on sequence variations and gene-expression analysis, the candidate genes underlying the major QTL for RKN resistance were pinpointed. They are Glyma10g02150 and Glyma10g02160, encoding a pectin methylesterase inhibitor and a pectin methylesterase inhibitor -pectin methylesterase, respectively. This QTL mapping approach not only combines SNP discovery, SNP validation, and genotyping, but also solves the issues caused by genome duplication and repetitive sequences. Hence, it can be widely used in crops with a reference genome to enhance QTL mapping accuracy.