Trilateral FPP 2023: Engineering Gene Regulation in Plants to Yield Predictable Expression

About This Grant

Improving agricultural practices to ensure food security and the supply of valuable bioproducts is vitally important for society. Agricultural practices could be improved by using biotechnology to manipulate crop traits or to replace expensive pesticides with plant-produced, affordable compounds. However, both approaches require engineering multiple genes or pathways to yield predictable outcomes. Thus far, predicting the expression in plants of synthetic genes and pathways, even those composed of well-characterized DNA sequences, remains a major challenge. Indeed, when individual pathway genes are assembled into larger designs, their performance becomes unpredictable because regulatory elements and genic regions that encode proteins show strong context-dependent properties. Moreover, plant biotechnology relies on only a handful of regulatory elements, often of bacterial and viral origin, that constitutively and ubiquitously drive gene expression, interfering with growth and reducing crop yields. This lack of programmable and tunable regulatory elements contributes to unpredictable gene expression through expression interference and silencing. Together, the results of this study will enable the construction of multi-gene cassettes in which the expression of each gene is induced in response to a specific stimulus and at a specified level to produce optimal pathway flux. These efforts will generate large numbers of programmable and tunable regulatory elements and combinations of elements for future synthetic biology efforts in plants. Beyond transgenes, the ability to ‘program’ plants with predictable expression characteristics will deliver breakthroughs in manipulating endogenous pathways that control plant growth and yields, thereby making feasible the targeted engineering of resilient and high-yielding crops. This project exploits a toolbox of novel experimental and computational strategies pioneered by this interdisciplinary collaborative team to make plant gene expression predictable by constructing programmable and tunable multi-gene cassettes that produce antifeedants and insect pheromones as alternatives to traditional pesticides. There are three objectives, each one testing novel hypotheses as to the causes of specific challenges: 1) to develop a large repertoire of programmable and tunable synthetic regulatory elements, massively parallel reporter assays, machine learning and in silico evolution will be used; 2) to control gene expression levels more precisely through insulation and RNA stabilization, recent innovations in vaccine research will be applied to identify and test plant-based insulators in multi-gene cassettes while innovative computational machine learning strategies will be used to design optimized gene versions with increased RNA stability and codon usage; and, 3) to determine expression-limiting features, genetic engineering and a new single-molecule method to explore chromatin architecture will be used to manipulate gene order and position, DNA shape, torsional stress, and nucleosome occupancy. All project outcomes, including but not limited to sequence data and genetic resources, will be available through deposition to long-term repositories, publication, and on request. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.