Manipulation of autophagic pathway represents a tremendous opportunity for designing climate-smart crops with improved yield and better adaptability to changing environment. For exploiting autophagy to its full potential, identification and comprehensive characterization of adapters/receptor complex and elucidation of its regulatory network in crop plants is highly warranted. Autophagy is a major intracellular trafficking pathway in eukaryotes involved in vacuolar degradation of cytoplasmic constituents, mis-folded proteins, and defective organelles. Under optimum conditions, autophagy operates at a basal level to maintain cellular homeostasis, but under stressed conditions, it is induced further to provide temporal stress relief. Our understanding of this highly dynamic process has evolved exponentially in the past few years with special reference to several plant-specific roles of autophagy. Here, we review the most recent advances in the field of autophagy in plants and discuss its potential implications in designing crops with improved stress and disease-tolerance, enhanced yield potential, and improved capabilities for producing metabolites of high economic value. We also assess the current knowledge gaps and the possible strategies to develop a robust module for biotechnological application of autophagy to enhance bioeconomy and sustainability of agriculture.
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http://dx.doi.org/10.1007/s00425-022-04004-z | DOI Listing |
BMC Plant Biol
January 2025
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia.
Background: Organic fertilizers are safer and more eco-friendly than chemical fertilizers; hence, organic fertilizers can be used to support sustainable farming. The effects of PGPRs are manifold in agriculture, especially in monoculture crops, where the soil needs to be modified to increase germination, yield, and disease resistance. The objective of this study was to assess the effects of PGPRs combined with fertilizer on the yield and productivity of canola.
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January 2025
Shanghai Key Laboratory of Agricultural Genetics and Breeding, Key Laboratory for Safety Assessment (Environment) of Agricultural Genetically Modified Organisms of Ministry of Agriculture and Rural Affairs (Shanghai), Biotechnology Research Institute of Shanghai Academy of Agricultural Sciences, Shanghai, 201106, China.
Salinization poses a significant challenge in agriculture. Identifying salt-tolerant plant germplasm resources and understanding their mechanisms of salt tolerance are crucial for breeding new salt-tolerant plant varieties. However, one of the primary obstacles to achieving this goal in crops is the physiological complexity of the salt-tolerance trait.
View Article and Find Full Text PDFSci Rep
January 2025
ICAR-Indian Institute of Agricultural Biotechnology, Namkum, Ranchi, 834010, India.
The present study investigates the supplemental effects of chia seed oil (CSO) on the growth performance and modulation of intestinal microbiota in Labeo rohita fingerlings. Four diets were formulated with graded levels of CSO: 1.0%, 2.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Botany, Lahore College for Women University, Lahore, Pakistan.
The present study was designed to highlight the ameliorative role of iron nanoparticles (FeNPs) against drought stress in spinach (Spinacia oleracea L.) plants. A pot experiment was performed in two-way completely randomize design with three replicates.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
January 2025
Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium. Electronic address:
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-destructive, and efficient tool for detecting mycotoxins in cereal crops and foods. This study aims to utilize vis-NIR spectroscopy, coupled with a feature selection technique and machine learning modelling, to predict and classify DON contamination in wheat kernels and flour.
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