Multilevel interactions between native and ectopic isoprenoid pathways affect global metabolism in rice.

Isoprenoids are natural products derived from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). In plants, these precursors are synthesized via the cytosolic mevalonate (MVA) and plastidial methylerythritol phosphate (MEP) pathways. The regulation of these pathways must therefore be understood in detail to develop effective strategies for isoprenoid metabolic engineering. We hypothesized that the strict regulation of the native MVA pathway could be circumvented by expressing an ectopic plastidial MVA pathway that increases the accumulation of IPP and DMAPP in plastids. We therefore introduced genes encoding the plastid-targeted enzymes HMGS, tHMGR, MK, PMK and MVD and the nuclear-targeted transcription factor WR1 into rice and evaluated the impact of their endosperm-specific expression on (1) endogenous metabolism at the transcriptomic and metabolomic levels, (2) the synthesis of phytohormones, carbohydrates and fatty acids, and (3) the macroscopic phenotype including seed morphology. We found that the ectopic plastidial MVA pathway enhanced the expression of endogenous cytosolic MVA pathway genes while suppressing the native plastidial MEP pathway, increasing the production of certain sterols and tocopherols. Plants carrying the ectopic MVA pathway only survived if WR1 was also expressed to replenish the plastid acetyl-CoA pool. The transgenic plants produced higher levels of fatty acids, abscisic acid, gibberellins and lutein, reflecting crosstalk between phytohormones and secondary metabolism.

Modeling the effects of strigolactone levels on maize root system architecture.

Maize is the most in-demand staple crop globally. Its production relies strongly on the use of fertilizers for the supply of nitrogen, phosphorus, and potassium, which the plant absorbs through its roots, together with water. The architecture of maize roots is determinant in modulating how the plant interacts with the microbiome and extracts nutrients and water from the soil. As such, attempts to use synthetic biology and modulate that architecture to make the plant more resilient to drought and parasitic plants are underway. These attempts often try to modulate the biosynthesis of hormones that determine root architecture and growth. Experiments are laborious and time-consuming, creating the need for simulation platforms that can integrate metabolic models and 3D root growth models and predict the effects of synthetic biology interventions on both, hormone levels and root system architectures. Here, we present an example of such a platform that is built using Mathematica. First, we develop a root model, and use it to simulate the growth of many unique 3D maize root system architectures (RSAs). Then, we couple this model to a metabolic model that simulates the biosynthesis of strigolactones, hormones that modulate root growth and development. The coupling allows us to simulate the effect of changing strigolactone levels on the architecture of the roots. We then integrate the two models in a simulation platform, where we also add the functionality to analyze the effect of strigolactone levels on root phenotype. Finally, using in silico experiments, we show that our models can reproduce both the phenotype of wild type maize, and the effect that varying strigolactone levels have on changing the architecture of maize roots.

A mathematical model for strigolactone biosynthesis in plants.

Strigolactones mediate plant development, trigger symbiosis with arbuscular mycorrhizal fungi, are abundant in 80% of the plant kingdom and help plants gain resistance to environmental stressors. They also induce germination of parasitic plant seeds that are endemic to various continents, such as Orobanche in Europe or Asia and Striga in Africa. The genes involved in the early stages of strigolactones biosynthesis are known in several plants. The regulatory structure and the latter parts of the pathway, where flux branching occurs to produce alternative strigolactones, are less well-understood. Here we present a computational study that collects the available experimental evidence and proposes alternative biosynthetic pathways that are consistent with that evidence. Then, we test the alternative pathways through in silico simulation experiments and compare those experiments to experimental information. Our results predict the differences in dynamic behavior between alternative pathway designs. Independent of design, the analysis suggests that feedback regulation is unlikely to exist in strigolactone biosynthesis. In addition, our experiments suggest that engineering the pathway to modulate the production of strigolactones could be most easily achieved by increasing the flux of ß-carotenes going into the biosynthetic pathway. Finally, we find that changing the ratio of alternative strigolactones produced by the pathway can be done by changing the activity of the enzymes after the flux branching points.