Expression Elements Derived From Plant Sequences Provide Effective Gene Expression Regulation and New Opportunities for Plant Biotechnology Traits

Abstract

Plant biotechnology traits provide a means to increase crop yields, manage weeds and pests, and sustainably contribute to addressing the needs of a growing population. One of the key challenges in developing new traits for plant biotechnology is the availability of expression elements for efficacious and predictable transgene regulation. Recent advances in genomics, transcriptomics, and computational tools have enabled the generation of new expression elements in a variety of model organisms. In this study, new expression element sequences were computationally generated for use in crops, starting from native Arabidopsis and maize sequences. These elements include promoters, 5′ untranslated regions (5′ UTRs), introns, and 3′ UTRs. The expression elements were demonstrated to drive effective transgene expression in stably transformed soybean plants across multiple tissues types and developmental stages. The expressed transcripts were characterized to demonstrate the molecular function of these expression elements. The data show that the promoters precisely initiate transcripts, the introns are effectively spliced, and the 3′ UTRs enable predictable processing of transcript 3′ ends. Overall, our results indicate that these new expression elements can recapitulate key functional properties of natural sequences and provide opportunities for optimizing the expression of genes in future plant biotechnology traits.

From qualitative to quantitative: the state of the art and challenges for plant synthetic biology

“…Recent studies have also shown the potential for fine‐tuning transgene expression levels by regulatory elements while spatiotemporal specificity is preserved [37,72,77–79]. In order to achieve quantitative expression at various levels, a rational design by combining multiple regulatory elements from various species, such as the core promoters, enhancers, 5′UTR, 3′UTR, and introns, was used to construct expression cassettes with quantitative intensities in multiple species and tissues [54,57,68,80,81]. The theoretical transcriptional activity (TTA) of cassettes with different promoter/terminator combinations is coincident with the experimental activity values in transiently transformed Nicotiana leaves [56].…”

“…Machine learning (ML) algorithms, such as support vector machine (SVM) or neural networks, greatly facilitate the qualitative/quantitative prediction and de novo design of bioparts. Sequence motifs identified by POsition‐sensitive WoRd Set (POWERS) algorithm are used to generate novel elements, whose property of expression is comparable to plant native elements across multiple tissues and developmental stages [81,82]. Another way to obtain customizable elements is de novo design.…”