Salinity stress is one of the major stressors that limits yield potential in field crops. Salinity-led imbalances in ionic and water potential, as well as oxidative damage, impair photosynthesis. Plant-growth-promoting rhizobacteria (PGPRs) have been demonstrated to mitigate salinity-stress-induced damage through various mechanisms such as biofilm and exopolysaccharide production, modulation of plant root architecture or molecular signaling involving modulation of sodium/potassium efflux transporters. PGPRs are known to induce biosynthesis and signaling of various phytohormones in plants. PGPR-derived phytohormones can in turn regulate molecular signaling involved in maintaining ion fluxes, preventing salinity-induced senescence, and reinforcing plant root architecture, thereby maintaining plant growth and development under saline conditions. In this review, we provide comprehensive advances on how PGPRs modulate and integrate biosynthesis and/or signaling of various phytohormones, such as auxins, cytokinins, gibberellin, ethylene, abscisic acid, salicylic acid, jasmonates, brassinosteroids and strigolactones, to reshape plant architecture, physiological and biochemical responses in plants under salinity. We integrate molecular evidence with morpho-physiological studies and propose a phytohormone-centric framework to select strains that optimize growth, ion homeostasis and plant stress resilience under salinity.
Arghyadeepa Moharana, Lochan Dhruw, Armita Chakraborty et al.· International Journal of Mol...· 0 citations
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Ha Duc Chu, T. Q. Nguyen, Loc Van Nguyen et al.· Genes· 1 citation
