Learning from nature: understanding and engineering transcription regulation in plants

Abstract

Synthetic biology offers tools to modify plants in an environment that is changing at a faster pace than can be matched by evolution alone. The tools available come from parts either borrowed from nature or designs built upon our understanding of foundational molecular mechanisms. Some of the indispensable tools in the synthetic biology toolkit involve ways to regulate transcription strength and transcription pattern. The ability to control when and where genes are turned on is essential to survival. By leveraging publicly available RNA-seq atlases, we were able to identify a set of some of the most stably expressed genes in the genome of the reference plant Arabidopsis thaliana. We evaluated these promoter parts in transient assays in Nicotiana benthamiana and in stable transgenic lines of Arabidopsis. To provide additional functionality to these promoter parts borrowed from nature, we introduced gRNA-target sites recognized by a dCas9-repressor construct to turn these constitutive promoters into repressible NOR logic gates in N. benthamiana. To explore the fundamental design rules behind constitutive promoters, we did an in silico experiment that expanded the RNA-seq atlas pipeline to identify stably expressed genes across multiple angiosperm species. Comparisons between core promoter architectures and gene expression stability revealed potential differences in core promoter usage in monocots and eudicots. Furthermore, evaluating groups of evolutionarily related promoters across species found a lack of strong evolutionary preference for core promoter types for expression stability. To improve upon the repression aspect of transcriptional regulation, we used machine learning models to predict and optimize a short alpha helical repression domain, and identified potential key residues that contribute to repression. Taken together, this work contributes to our ability to engineer transcriptional regulation in plants by providing a new set of tools, as well as revealing design rules behind both gene expression pattern and repression.