Investigating the Effect of Putative Cytokinin Antagonists on Root Growth in Rice, and their Efficacy in Mitigating Stress

Heap, Brittany and McAinsh, Martin and Toledo-Ortiz, Gabriela (2022) Investigating the Effect of Putative Cytokinin Antagonists on Root Growth in Rice, and their Efficacy in Mitigating Stress. PhD thesis, Lancaster University.

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Abstract

There is a plethora of challenges that must be addressed this century to ensure the demand for food, fodder and biofuel is met. Feeding 9 billion people whilst counteracting the negative effects that erratic and more severe weather events are having due to climate change is a challenge that requires innovative approaches. Drought and salinity are significant limiting factors to crop yields, and modifying plant traits to avoid these stresses has been identified as a method of improving crop productivity. This study investigated the ability of putative root-specific cytokinin antagonists, molecules that block activity of the plant hormone cytokinin, to promote root growth in rice as a mechanism for improving crop abiotic stress tolerance. In addition to the parent compound, four novel compounds synthesised by Globachem Discovery Ltd. were found to promote root growth of the rice variety, Nipponbare, in liquid media. Subsequently, seed priming was established as a way of applying the compounds, significantly reducing preparation time and the quantity of product required. The long-term effects of priming were found to not affect aboveground biomass but did confer a negative effect to yield. The compounds were also tested for their ability to promote root growth in commercially relevant rice varieties and growth settings under drought and salt stress. However, the increase in root length found in Nipponbare was not observed in a commercial setting or commercially used rice varieties under optimum or stress conditions, highlighting the high specificity of the compounds. These findings show that whilst there is potential for these compounds to promote root growth, their use must be further optimised for agricultural purposes. In parallel to the lab-based studies, three models were designed and implemented in Chapters 2, 4 and 5. A machine learning technique was used to predict the likelihood of a compound having biological activity, based on its chemical properties. In a subsequent chapter the effects of spatial heterogeneity within a glasshouse were quantified and accounted for statistically. Finally, geospatial modelling was used to identify key regions where plant growth regulators could be applied most effectively. These models allow the optimisation of current practice, from agrochemical design to dissemination of a product, thereby contributing to a more robust agricultural system. The lab-based assays and different modelling approaches used in this study highlight the multi-faceted and collaborative approaches that are required to tackle the pressing humanitarian and environmental challenges of this century. This study goes some way to addressing these challenges.