Climate change and environmental stresses pose severe, multifaceted risks to global food security and environmental sustainability, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Among other approaches, priming constitutes an easy and relatively cheap strategy due to its potential to enhance germination and stress resilience under changing environments. This review examines an emerging shift in crop improvement, in which environmental stress is no longer viewed solely as a constraint but also as a potential tool for enhancing plant resilience through stress priming and molecular memory. Various priming strategies applied through methods such as hydropriming, osmopriming, hormonal priming, chemical priming, thermopriming, biopriming, and nanopriming, effectively enhance germination performance and stress tolerance through activated defense pathways, osmolyte accumulation, and antioxidant system modulation. Advances in transcriptomics, metabolomics, and proteomics have revealed key markers of the primed state, including gene expression changes, metabolite accumulation, and epigenetic programming, which can provide tools for selection. These markers offer valuable opportunities for identifying and selecting genotypes with enhanced priming responsiveness. Integrating priming technologies with modern breeding strategies, particularly genomic selection, may therefore provide a powerful framework for improving stress adaptation in crops. By combining physiological priming with advanced genomic tools, this approach offers a practical and cost-effective route to accelerate the development of climate-resilient crop varieties and support sustainable agricultural production under increasingly variable environmental conditions.
The global food system is constantly being constrained by biotic and abiotic challenges, resulting in instability and insecurity, particularly in regions like sub-Saharan Africa, where agricultural productivity often remains below global averages. Microbial biotechnology includes many sustainable ways of leveraging the metabolic potential of microorganisms, such as bacteria, fungi, and viruses, to address food insecurity through enhanced crop resilience, precision fermentation, and improved soil health. Keeping up with these demands now requires constant innovative multidisciplinary approaches in the fast-growing field of artificial intelligence (AI). AI is reinventing microbial biotechnology via various applications in the areas of taxonomic profiling, metabolic modeling, and the design of microbial cell factories. This review evaluates the transformative role of AI in optimizing these microbial systems. Current advancements showcase the use of machine learning and deep learning architectures, such as convolutional neural networks and transformers, to accelerate the discovery of novel biofertilizers and biocontrol agents. In precision fermentation, AI-driven models and reinforcement learning are increasingly used to optimize the clustered regularly interspaced short palindromic repeats (CRISPR)-based microbial engineering, as well as bioprospecting for microbes that can improve soil health. However, challenges that beset the current landscape still include overall adoption, difficult-to-understand models or algorithm interpretability, quality input of training data, good ethical practices, high computational cost associated with complex structural simulations, and the need for standardized processes to make sure that AI applications are reliable and applicable in different microbiological settings. While AI is an essential ingredient for futuristic microbial biotechnology, tackling these technical and ethical hurdles is key to achieving stable food security.
K. A. Oyeniran, Lara Donaldson· Frontiers in Bacteriology· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
