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Physiological and molecular responses of bread wheat and its wild relative species to drought stress
Analysis of Physio-Biochemical Responses and Expressional Profiling Antioxidant-Related Genes in Some Neglected Aegilops Species under Salinity Stress
Wild common wheat species represent a significant pool of resistance genes to various environmental stresses. In this study, we examined several physiological traits and the activity of three antioxidant enzymes—namely, catalase (CAT), superoxide dismutase (SOD), and ascorbate peroxidase (APX)—as well as the expression patterns of their encoding genes in three neglected Aegilops species with alien genomes (including Ae. triuncialis (UUCC-genome), Ae. neglecta (UUMM-genome) and Ae. umbellulata (UU-genome)) under two control (0 mM NaCl) and salinity (250 mM NaCl) conditions. The results of the analysis of variance (ANOVA) showed highly significant effects of salinity stress, accessions, and their interaction on most physio-biochemical traits, root and shoot dry biomasses, and antioxidant-related gene expression level. As a result of comparison between Aegilops species and a bread wheat cultivar (cv. Narin as a salt-tolerant reference variety), Ae. triuncialis responded well to salinity stress, maintaining both ionic homeostasis capability and biochemical ability. Moreover, transcriptional data revealed the prominence of Ae. triuncialis over other Aegilops species and salt-tolerant bread wheat [cv. Narin] in terms of the level of expression of antioxidant genes (APX, SOD, and CAT). This result was further supported by a biplot rendered based on principal component analysis (PCA), where this wild relative showed a positive association with most measured traits under salinity stress. Moreover, we speculate that this accession can be subjected to physiological and molecular studies, and that it can provide new insights into the use of the alien genomes in future wheat breeding programs.
Mammalian Sex Hormones as Steroid-Structured Compounds in Wheat Seedling: Template of the Cytosine Methylation Alteration and Retrotransposon Polymorphisms with iPBS and CRED-iBPS Techniques
Phytohormones are chemical compounds found naturally in plants that have a significant effect on their growth and development. The increase in research on the occurrence of mammalian sex hormones (MSHs) in plants has prompted the need to investigate the functions performed by these hormones in plant biology. In the present study, we investigated the effects of MSHs on DNA damage and DNA methylation of wheat (Triticum aestivum L.) during the seedling growth stage, using the CRED-iPBS (coupled restriction enzyme digestion/inter primer binding site) assay and iPBS analysis to determine DNA methylation status. Exogenous treatment with four MSHs (17-β-estradiol, estrogen, progesterone, and testosterone) was carried out at four different concentrations (0, 0.05, 0.5, and 5 µM). The highest genomic template stability (GTS) value (80%) was observed for 5 µM 17-β-estradiol, 0.5 µM testosterone, and 0.05 µM estrogen, while the lowest value (70.7%) was observed for 5 µM progesterone and 0.5 µM estrogen. The results of the CRED-iPBS analysis conducted on MspI indicate that the 0.05 µM estrogen-treated group had the highest polymorphism value of 40%, while the 5 µM progesterone-treated group had the lowest value of 20%. For HpaII, treatment with 0.5 µM 17-β-estradiol had the highest polymorphism value of 33.3%, while the group treated with 0.05 µM 17-β-estradiol and 0.05 µM progesterone had the lowest value of 19.4%. In conclusion, MSH treatments altered the stability of the genomic template of wheat plants and affected the cytosine methylation status at the seedling growth stage. Upon comprehensive examination of the results, it was seen that the employed methodology successfully detected alterations in cytosine methylation of genomic DNA (gDNA), as well as changes in the pattern of genomic instability.
Effect of Different Plant Growth-Promoting Rhizobacteria on Biological Soil Properties, Growth, Yield and Quality of Oregano (Origanum onites L.)
Intensive agriculture uses continuous chemical fertilizers to increase crop yields, but excessive use of fertilizers leads to environmental pollution, permanent changes in physicochemical conditions in soil ecology, deterioration of soil biological health, leaching of nutrients, surface and groundwater pollution and eutrophication. Plant growth-promoting rhizobacteria (PGPR) are becoming increasingly important for ensuring crop safety, increasing nutrient uptake and output, lowering fertilizer costs, preventing environmental contamination and promoting sustainable agriculture and agricultural resources. Therefore, the purpose of this study was to identify and evaluate the effects of fifteen bacteria strains that were isolated from various acidic rhizospheric soils as biofertilizers on soil biological properties. Growth, yield and quality traits were analyzed, and various PGPR were identified using 16S ribosomal RNA of Turkish oregano. Fifteen bacterial inoculations with 1-aminocyclopropane-1-carboxylate (ACC) deaminase, N2-fixing, P-solubilizing and/or IAA-producing genes were used in the experiment, which was carried out in a randomized block design with five replicates (each with three pots) and a control without inoculation. Increased biological activity in soil inoculated with bacteria with multiple traits was confirmed by high C and N content in microbial biomass, urease, dehydrogenase and acid and alkaline phosphatase activities. Essential oil content, oil yield, thymol and carvacrol contents increased by 0.5–40.1%, 5.9–71.9%, 0.07–16.7% and 0.3–9.2%, respectively, as a result of bacterial inoculation. Oil content ranged from 2.02% to 2.83%; carvacrol (66.1–72.2%) was the main constituent, followed by thymol (14.5–16.9%) and linalool (1.38–3.68%). Two large PGPR groups were formed based on genetic distance analysis. Responses were variable and depended on the inoculant strain and the parameters being evaluated. The results indicate PGPR has clear potential for improving the yield of cultivated aromatic and essential oil plants, such as oregano.
Identification of High Yielding and Stable Barley Genotypes for Drought Conditions in the Moderate Climate of Iran Using AMMI Model and GGE Biplot Analysis
Dataset for unrevealing the application of multi-trait genotype-ideotype distance index and multi-trait index based on factor analysis and ideotype-design models in the identification of high-yielding and stable barley genotypes
Cross-talk between stability parameters and selection models: a new procedure for improving the identification of the superior genotypes in multi-environment trials
Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses
Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.