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Physiological and molecular responses of bread wheat and its wild relative species to drought stress
Genetic diversity and population structure of Iranian oak (Quercus spp.) accessions based on ISSR and CBDP markers
Selection of High-Yielding and Stable Genotypes of Barley for the Cold Climate in Iran
The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019–2020 and 2020–2021). The results of the freezing test (LT50) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran.
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.
A Framework for Selection of High-Yielding and Drought-tolerant Genotypes of Barley: Applying Yield-Based Indices and Multi-index Selection Models
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.
Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method
Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.
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.
Physiological and molecular responses of wild relatives of wheat possessing the D genome to salinity stress
Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.)
The assessment of 1000-kernel weight holds significant importance in determining maize grain yield, and elucidating its underlying genetic mechanisms is imperative for enhancing its overall performance. The material for the study consisted of 26 doubled-haploid (DH) maize lines obtained from crossing two cultivars with flint kernels. Lines were planted in the northern part of the Lower Silesia voivodship in Poland over ten years (2013–2022). The 1000-kernel weight was assessed. The purposes of the research were as follows: (1) to assess genotype by environment interaction (GEI by the additive main effects and multiplicative interaction (AMMI) model; (2) the selection of stable DH lines and environment-specific lines; and (3) the estimation of parameters related to additive and additive–additive gene interaction (epistasis). The results indicate the significant effects of genotype and environment, as well as the GEI, on the 1000-kernel weight. Estimates of additive gene action effects were statistically significant in every year of the study, except 2022. Estimates of epistasis (total additive-by-additive interaction) effects for 1000-kernel weight were statistically significant in 2013, 2015, and 2017 (positive effects), as well as in 2018 and 2020 (negative effects). The lines KN07 and KN10 are recommended for further inclusion in the breeding program due to their stability and highest average of 1000-kernel weight.
Cross-talk between stability parameters and selection models: a new procedure for improving the identification of the superior genotypes in multi-environment trials
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
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.