Assessment of tobacco and N. benthamiana as biofactories of irregular monoterpenes for sustainable crop protection

Irregular monoterpenes are important precursors of different compounds employed in pest control such as insecticides and insect sex pheromones. Metabolically engineered plants are appealing as biofactories of such compounds, but specially as potential live biodispensers of related bioactive volatiles, which could be continuously emitted to the environment from different plant tissues. Here we assess the use of cultivated tobacco and Nicotiana benthamiana as biofactories for the irregular monoterpenes chrysanthemol and lavandulol. We evaluate the impact of high levels of constitutive metabolite production on the plant physiology and biomass, and their biosynthetic dynamics for different plant tissues and developmental stages. As an example of an active pheromone compound, we super-transformed the best lavandulol-producing tobacco line with an acetyl transferase gene to obtain a tobacco lavandulyl acetate biodispenser producing up to 0.63 mg of lavandulyl acetate per plant every day. We estimate that with these volatile emission levels, between 200 and 500 plants per hectare would be sufficient to ensure a daily emission of pheromones comparable to commercial lures. This is an important step towards plant-based sustainable solutions for pest control, and it lays the ground for further developing biofactories for other irregular monoterpenoid pheromones, whose biosynthetic genes are yet unknown. The dataset includes GC-MS data (peak areas quantified for each compound with its Qi, normalized) for all the figures and supplementary figures of the associated manuscript. Peak areas are normalized with an admixture, analyzed on the same day as the samples. It also includes biomass data and measurements for the analyzed plants (plant height, leaf biomass, days to flowering time). File S3 shows data for quantitative PCR to determine transgene copy number, and for qRT-PCR to measure transgene expression levels. For all datasets, statistical analysis is also included to support the conclusions showed in the graphs.