Synthetic Memory Circuits for Stable Cell Reprogramming in Plants

Plant biotechnology predominantly relies on a restricted set of genetic parts with limited capability to customize spatiotemporal and conditional expression patterns. Synthetic gene circuits have the potential to integrate multiple customizable input signals through a processing unit constructed from biological parts to produce a predictable and programmable output. Here we present a suite of functional recombinase-based gene circuits for use in plants. We first established a range of key gene circuit components compatible with plant cell functionality. We then used these to develop a range of operational logic gates using the identify function (activation) and negation function (repression) in Arabidopsis protoplasts and in vivo, demonstrating their utility for programmable manipulation of transcriptional activity in a complex multicellular organism. Specifically, using recombinases and plant control elements, we activated transgenes in YES, OR and AND gates and repressed them in NOT, NOR and NAND gates; we also implemented the A NIMPLY B gate that combines activation and repression. Through use of genetic recombination, these circuits create stable long-term changes in expression and recording of past stimuli. This highly compact programmable gene circuit platform provides new capabilities for engineering sophisticated transcriptional programs and previously unrealized traits into plants.

A great deal of success has been made in plant biotechnology with relatively simple genetic tools, such as strong constitutive promoters. However, continuous overexpression of a gene across a whole plant can be detrimental to its growth patterns. Natural gene regulatory systems can integrate multiple signals to activate or repress transcriptional output, but currently, few tools allow sophisticated spatiotemporal control of transgenes in plants. Synthetic gene circuits are a promising approach to overcome this limitation. Ideally, gene circuits would take multiple input signals and integrate them into a genetic processing unit to control an output gene’s expression in a user-defined manner. These circuits aim to function in a manner analogous to natural gene regulatory networks, which can be extremely complex, with multiple inputs, outputs, and crosstalk between factors. Some natural gene regulatory units closely resemble simple Boolean logic gates. For example, the lac operon in Escherichia coli approximates an A NIMPLY B Boolean logic gate, with lactose activating expression but glucose repressing expression, overriding the presence of lactose. Engineering and application of synthetic logic gates in plants could provide us with the ability to tailor cellular activity and growth, programming them with sophisticated traits that cannot be achieved by conventional transcriptional control technologies.

In bacteria, yeasts and mammalian cells, transcription factors and recombinases have been used to construct gene circuits in cell culture. However, in plants, only a limited number of circuits have been developed to date. These include the split-TALE to act as an AND gate, the recombinase-based toggle switch and an elegant system using a combination of bacterial transcription factors to generate various types of logic. Although important advances, these circuit designs are, however, either limited in their range of logic and application or have output activity that is coupled to the persistent presence of the input signal(s). Alternative memory gene circuit technologies that drive output activity that persists beyond input signal presence are, therefore, needed for applications that are dependent on long-lived changes in gene expression.

Cell fate decisions in plant development require a cellular memory of specific experiences. This is challenging to replicate in synthetic gene circuits, as the effect is often transient due to the targeted expression change being temporally linked to the inducing signal. However, with recombination-based gene circuits, an altered expression pattern and resulting cell state can be locked in. Rather than doing this with epigenetic changes, as done in nature, recombination-based circuits achieve this by permanent changes to the genome in a specific subset of cells. Therefore, this system is more akin to the adaptive immune system of vertebrates that creates unique T cell receptors and antibodies via recombination than to a traditional cell fate mechanism that can be reversed. Recombinase-based gene circuits can, therefore, take an analog input signal and convert it into a digital signal.

In mathematical terminology, the identify function describes what happens when an input being on causes the output to switch on, also known as a YES or BUFFER gate. In terms of gene expression, this is gene activation from a stimulatory signal. In contrast, the mathematical negation (NOT) function would be equivalent to gene repression from a suppressive signal. To build all the basic Boolean logic gates as gene circuits, both the identify and negation functions need to be implemented using biological parts. The ‘Boolean logic and arithmetic through DNA excision’ (BLADE) system, developed in mammalian cell culture systems, uses recombinases to either activate a gene by removing a terminator sequence between the promoter and coding sequence of the output gene or repress gene expression by removing a part of the output gene. Such a recombinase-based design means that the switch from one transcriptional state to another is long-term and stable, and, thus, the continuous addition of the activating signal is not required, unlike gene circuit designs based on transcription factors. Given the limited range of plant circuits for the control of plant cellular activity and development, especially with respect to circuits able to generate long-term stable changes in gene expression, we aimed, in this study, to develop, optimize and implement effective recombinase-based synthetic gene circuits to enable new capabilities for sophisticated plant engineering that requires memory-based functions.