Phenolic compounds (PCs) affect the bread quality and can also affect the other types of end-use food products such as chapatti (unleavened flat bread), now globally recognized wheat-based food product. The detailed analysis of PCs and their biosynthesis genes in diverse bread wheat (Triticum aestivum) varieties differing for chapatti quality have not been studied. In this study, the identification and quantification of PCs using UPLC-QTOF-MS and/or MS/MS and functional genomics techniques such as microarrays and qRT-PCR of their biosynthesis genes have been studied in a good chapatti variety, “C 306” and a poor chapatti variety, “Sonalika.” About 80% (69/87) of plant phenolic compounds were tentatively identified in these varieties. Nine PCs (hinokinin, coutaric acid, fertaric acid, p-coumaroylqunic acid, kaempferide, isorhamnetin, epigallocatechin gallate, methyl isoorientin-2′-O-rhamnoside, and cyanidin-3-rutinoside) were identified only in the good chapatti variety and four PCs (tricin, apigenindin, quercetin-3-O-glucuronide, and myricetin-3-glucoside) in the poor chapatti variety. Therefore, about 20% of the identified PCs are unique to each other and may be “variety or genotype” specific PCs. Fourteen PCs used for quantification showed high variation between the varieties. The microarray data of 44 phenolic compound biosynthesis genes and 17 of them on qRT-PCR showed variation in expression level during seed development and majority of them showed low expression in the good chapatti variety. The expression pattern in the good chapatti variety was largely in agreement with that of phenolic compounds. The level of variation of 12 genes was high between the good and poor chapatti quality varieties and has potential in development of markers. The information generated in this study can be extended onto a larger germplasm set for development of molecular markers using QTL and/or association mapping approaches for their application in wheat breeding.

No abstract

Crop models assist in generating crop productivity trends under future climate and different irrigation water supply scenarios. In this study, the AquaCrop model with a salinity module to simulate the grain yield and water productivity of four wheat varieties, including three salt-tolerant (i. ) for grain yield, were 0.85, 0.96, 0.94 and for biomass 0.7, 0.95, 0.95, respectively, for all varieties and salinity levels. It was observed that the AquaCrop model was better at predicting the grain yield compared to biomass and water productivity for all varieties and salinity levels.

BackgroundStarch is a major part of cereal grain. It comprises two glucose polymer fractions, amylose (AM) and amylopectin (AP), that make up about 25 and 75 % of total starch, respectively. The ratio of the two affects processing quality and digestibility of starch-based food products. Digestibility determines nutritional quality, as high amylose starch is considered a resistant or healthy starch (RS type 2) and is highly preferred for preventive measures against obesity and related health conditions. The topic of nutrition security is currently receiving much attention and consumer demand for food products with improved nutritional qualities has increased. In bread wheat (Triticum aestivum L.), variation in amylose content is narrow, hence its limited improvement. Therefore, it is necessary to produce wheat lines or populations showing wide variation in amylose/resistant starch content. In this study, a set of EMS-induced M4 mutant lines showing dynamic variation in amylose/resistant starch content were produced. Furthermore, two diverse mutant lines for amylose content were used to study quantitative expression patterns of 20 starch metabolic pathway genes and to identify candidate genes for amylose biosynthesis.ResultsA population comprising 101 EMS-induced mutation lines (M4 generation) was produced in a bread wheat (Triticum aestivum) variety. Two methods of amylose measurement in grain starch showed variation in amylose content ranging from ~3 to 76 % in the population. The method of in vitro digestion showed variation in resistant starch content from 1 to 41 %. One-way ANOVA analysis showed significant variation (p < 0.05) in amylose and resistant starch content within the population. A multiple comparison test (Dunnett’s test) showed that significant variation in amylose and resistant starch content, with respect to the parent, was observed in about 89 and 38 % of the mutant lines, respectively. Expression pattern analysis of 20 starch metabolic pathway genes in two diverse mutant lines (low and high amylose mutants) showed higher expression of key genes of amylose biosynthesis (GBSSI and their isoforms) in the high amylose mutant line, in comparison to the parent. Higher expression of amylopectin biosynthesis (SBE) was observed in the low amylose mutant lines. An additional six candidate genes showed over-expression (BMY, SPA) and reduced-expression (SSIII, SBEI, SBEIII, ISA3) in the high amylose mutant line, indicating that other starch metabolic genes may also contribute to amylose biosynthesis.ConclusionIn this study a set of 101 EMS-induced mutant lines (M4 generation) showing variation in amylose and resistant starch content in seed were produced. This population serves as useful germplasm or pre-breeding material for genome-wide study and improvement of starch-based processing and nutrition quality in wheat. It is also useful for the study of the genetic and molecular basis of amylose/resistant starch variation in wheat. Furthermore, gene expression analysis of 20 starch metabolic genes in the two...