Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads.

Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high-expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible.

Dynamic control of endogenous metabolism with combinatorial logic circuits.

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences.

A mRNA's translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in Escherichia coli. We found that the mRNAs' translation rates were repressed, by up to 530-fold, when mRNA structures overlapped with the ribosome's footprint. In contrast, when the mRNA structure was located outside the ribosome's footprint, translation was repressed by <2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.

Genetic circuit characterization and debugging using RNA-seq.

Genetic circuits implement computational operations within a cell. Debugging them is difficult because their function is defined by multiple states (e.g., combinations of inputs) that vary in time. Here, we develop RNA-seq methods that enable the simultaneous measurement of: (i) the states of internal gates, (ii) part performance (promoters, insulators, terminators), and (iii) impact on host gene expression. This is applied to a three-input one-output circuit consisting of three sensors, five NOR/NOT gates, and 46 genetic parts. Transcription profiles are obtained for all eight combinations of inputs, from which biophysical models can extract part activities and the response functions of sensors and gates. Various unexpected failure modes are identified, including cryptic antisense promoters, terminator failure, and a sensor malfunction due to media-induced changes in host gene expression. This can guide the selection of new parts to fix these problems, which we demonstrate by using a bidirectional terminator to disrupt observed antisense transcription. This work introduces RNA-seq as a powerful method for circuit characterization and debugging that overcomes the limitations of fluorescent reporters and scales to large systems composed of many parts.

Automated physics-based design of synthetic riboswitches from diverse RNA aptamers.

Riboswitches are shape-changing regulatory RNAs that bind chemicals and regulate gene expression, directly coupling sensing to cellular actuation. However, it remains unclear how their sequence controls the physics of riboswitch switching and activation, particularly when changing the ligand-binding aptamer domain. We report the development of a statistical thermodynamic model that predicts the sequence-structure-function relationship for translation-regulating riboswitches that activate gene expression, characterized inside cells and within cell-free transcription-translation assays. Using the model, we carried out automated computational design of 62 synthetic riboswitches that used six different RNA aptamers to sense diverse chemicals (theophylline, tetramethylrosamine, fluoride, dopamine, thyroxine, 2,4-dinitrotoluene) and activated gene expression by up to 383-fold. The model explains how aptamer structure, ligand affinity, switching free energy and macromolecular crowding collectively control riboswitch activation. Our model-based approach for engineering riboswitches quantitatively confirms several physical mechanisms governing ligand-induced RNA shape-change and enables the development of cell-free and bacterial sensors for diverse applications.

Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism.

RNA folding plays an important role in controlling protein synthesis as well as other cellular processes. Existing models have focused on how RNA folding energetics control translation initiation rate under equilibrium conditions but have largely ignored the effects of nonequilibrium RNA folding. We introduce a new mechanism, called "ribosome drafting", that explains how a mRNA's folding kinetics and the ribosome's binding rate collectively control its translation initiation rate. During cycles of translation, ribosome drafting emerges whenever successive ribosomes bind to a mRNA faster than the mRNA can refold, maintaining it in a nonequilibrium state with an acceleration of protein synthesis. Using computational design, time-correlated single photon counting, and expression measurements, we demonstrate that slow-folding and fast-folding RNA structures with equivalent folding energetics can vary protein synthesis rates by 1000-fold. We determine the necessary conditions for ribosome drafting by characterizing mRNAs with rationally designed ribosome binding rates, folding kinetics, and folding energetics, confirming the predictions of a nonequilibrium Markov model of translation. Our results have widespread implications, illustrating how competitive folding and assembly kinetics can shape the gene expression machinery's sequence-structure-function relationship inside cells.

Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites.

The ribosome's interactions with mRNA govern its translation rate and the effects of post-transcriptional regulation. Long, structured 5' untranslated regions (5' UTRs) are commonly found in bacterial mRNAs, though the physical mechanisms that determine how the ribosome binds these upstream regions remain poorly defined. Here, we systematically investigate the ribosome's interactions with structured standby sites, upstream of Shine-Dalgarno sequences, and show that these interactions can modulate translation initiation rates by over 100-fold. We find that an mRNA's translation initiation rate is controlled by the amount of single-stranded surface area, the partial unfolding of RNA structures to minimize the ribosome's binding free energy penalty, the absence of cooperative binding and the potential for ribosomal sliding. We develop a biophysical model employing thermodynamic first principles and a four-parameter free energy model to accurately predict the ribosome's translation initiation rates for 136 synthetic 5' UTRs with large structures, diverse shapes and multiple standby site modules. The model predicts and experiments confirm that the ribosome can readily bind distant standby site modules that support high translation rates, providing a physical mechanism for observed context effects and long-range post-transcriptional regulation.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage.

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.