Indigenous wheat varieties continue to be cultivated and preserved due to their climatic adaptability and distinctive flavor contributions to cultural cuisines. This study covers the research gap by investigating the natural bioactive and antioxidant compounds present in Turkish indigenous wheat varieties. To this aim, the phenolic composition, ash contents, and antioxidant capacity were investigated in eighteen different indigenous Turkish wheat varieties (4 monococcum, 3 dicoccum, 4 durum, and 7 aestivum genotypes). An overall comprehensive analysis was carried out using statistical tools such as Heatmap and PCA (Principal component analysis). The results indicated that dicoccum genotype exhibited highest soluble free (49.04 mg) and soluble conjugated phenolics (188.54 mg), whereas aestivum was found rich in insoluble bound (873.89 mg), total phenolic (1058.35 mg GAE/kg dm), and total flavonoid (380.43 mg CE/kg dm) contents. Moreover, monococcum, dicoccum, and durum genotypes exhibited non-significantly higher total antioxidant capacities compared to the aestivum wheat genotype. In conclusion, indigenous wheat varieties, particularly from the dicoccum and aestivum genotypes, have the potential to be a significant source of phenolic and antioxidant compounds. These findings suggest promising prospects for the selection of indigenous wheat varieties under on-farm conservation programs for future commercialization and breeding programs.
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http://dx.doi.org/10.1002/fsn3.4614 | DOI Listing |
Septoria tritici blotch (STB) is a predominant foliar disease of wheat, caused by the pathogen Zymoseptoria tritici. This disease can lead to substantial yield losses warranting control by using expensive fungicides. One effective method of STB control is the utilization of resistant wheat varieties.
View Article and Find Full Text PDFPlant Mol Biol
March 2025
College of Agriculture, Guizhou University, Guiyang, Guizhou, China.
Climate change, with its increasing temperatures, is significantly disrupting global agricultural systems, and wheat, a key cereal crop faces severe challenges. Heat stress has emerged as a critical threat, accelerating wheat growth, leading to premature maturation, reduced grain filling, and ultimately lower yields. The situation is exacerbated by more frequent and intense heat waves, particularly in regions already struggling with water scarcity.
View Article and Find Full Text PDFG3 (Bethesda)
March 2025
International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico.
Genomic selection (GS) is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy (PA) for grain yield (GY) across various selection environments (SEs) using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of GY data from six SEs were analyzed, with the populations of five years (2018-2023) as the validation population (VP) and earlier years (back to 2013-2014) as the training population (TP).
View Article and Find Full Text PDFFront Cell Dev Biol
February 2025
College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China.
The Notch signaling pathway is an evolutionarily conserved intercellular signaling cascade that regulates a number of cellular processes, including cell development, proliferation, apoptosis, and genome stability. The Notch signaling pathway is pervasive in the human body, affecting tumorigenesis and progression, which is one of the most significant signaling pathways in this regard, influencing various receptors and cellular functions of tumor cells. Aberrant expression or mutation of Notch has been linked to the onset and progression of a variety of malignant tumors.
View Article and Find Full Text PDFAdv Sci (Weinh)
March 2025
State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, Beijing, 100081, China.
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype.
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