Genomic prediction of hybrid performance in grain sorghum (Sorghum bicolor L.)

Advancing Climate-Resilient Sorghum: the Synergistic Role of Plant Biotechnology and Microbial Interactions

Climate-related problems such as drought stress, extreme temperature, erratic rainfall patterns, soil degradation, heatwaves, flooding, water logging, pests and diseases afflict the production and sustainability of sorghum. These challenges may be addressed by adopting climate-resilient practices and using advanced agronomic techniques. These challenges are being addressed through innovative applications of plant biotechnology and microbiology, which offer targeted solutions to enhance sorghum's resilience. For instance, biotechnological tools like CRISPR/Cas9 enable precise genetic modifications to improve drought and heat tolerance, while microbial inoculants, such as plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF), enhance nutrient uptake and stress tolerance through symbiotic interactions. However, biotechnological tools lead to the development of sorghum varieties with heat, drought and salinity tolerance, while marker-assisted selection significantly accelerates breeding for stress-resilient traits. When genetic engineering is introduced, genes encoding heat shock proteins, Osmo protectants and antioxidant pathways are introduced to increase plant resistance to abiotic stress. These compounds stabilise cellular structures, protect enzymes, and maintain osmotic balance, enhancing the plant's ability to survive and function in adverse environmental conditions. At the same time, it is reported that microbiology offers beneficial microbes, nitrogen-fixing bacteria, phosphate-solubilizing microorganisms, and arbuscular mycorrhizal fungi that help enhance nutrient availability, soil health and water uptake. Combinations of endophytes and microbial inoculants enhance plant immunity to pests and diseases while increasing tolerance to stress. Biocontrol agents such as Bacillus and Trichoderma contain suppression of pathogens and need less dependence on the use of chemical pesticides. On top of that, genetic modification increases the nutritional quality of sorghum biofortified. This is where biotechnology and microbiology work together to deliver sustainable farming systems reducing environmental impacts, boosting yields and securing food supply under environmental stresses. This review aims to examine the synergistic integration of plant biotechnology and microbial interactions as a strategy to enhance sorghum's resilience to climate-induced stresses, including drought, elevated temperatures, and nutrient-deficient soils. It highlights recent advancements in biotechnological tools such as gene editing, marker-assisted selection, and tissue culture, alongside the emerging role of plant-beneficial microbes in promoting stress tolerance and improving soil health. By synthesizing current knowledge across these disciplines, this review seeks to outline a framework for future research that harnesses the intersection of biotechnology and microbial ecology to support the sustainable improvement of sorghum resilience.

Emerging frontiers in sorghum genetic engineering

Sorghum, a climate-resilient cereal, is crucial for meeting the growing demand for food and feed in arid and semi-arid regions, especially amid global population growth and climate change. Despite its natural drought tolerance and adaptability, sorghum faces challenges in increasing yield, enhancing resistance to abiotic and biotic stresses, and improving grain quality. Genetic engineering has emerged as a powerful tool to address these challenges by directly modifying genes associated with desirable traits. Recent advancements have utilized morphogenic regulators to improve transformation and regeneration efficiency in sorghum. This review explores the status of genomic resources and genetic diversity in sorghum, highlighting the advancements and challenges faced in its genetic engineering efforts. Genome editing technologies, particularly CRISPR/Cas systems, have improved key agronomic traits such as stress tolerance, nutrient use efficiency, and grain quality. However, significant obstacles still need to be addressed, including low regeneration rates, high genotype dependency, and labor-intensive transformation processes. We highlight potential strategies to overcome these barriers, such as optimizing transformation protocols, exploring alternative explants, using morphogenic regulators and advancing tissue culture techniques. Additionally, we discuss the biosafety considerations and potential applications of genetically engineered sorghum in global agriculture. This review underscores the need for ongoing innovation to unlock the potential of genetically engineered sorghum in addressing global food security challenges.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.