Robust, tunable genetic memory from protein sequestration combined with positive feedback

Customizing cellular signal processing by synthetic multi-level regulatory circuits

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Characterizing a Propionate Sensor in E. coli Nissle 1917

Short-chain fatty acids (SCFAs) are commonly found in the large intestine, but generally not in the small intestine, and influence microbiome composition and host physiology. Thus, synthetic biologists are interested in developing engineered probiotics capable of in situ detection of SCFAs as biogeography or disease sensors. One SCFA, propionate, is both sensed and consumed by E. coli. Here, we utilize the E. coli transcription factor PrpR, sensitive to the propionate-derived metabolite (2S,3S)-2-methylcitrate, and its cognate promoter PprpBCDE to detect extracellular propionate with the probiotic chassis bacterium E. coli Nissle 1917. We identify that PrpR-PprpBCDE displays stationary phase leakiness and transient bimodality, and we explain these observations through evolutionary rationales and deterministic modeling, respectively. Our results will help researchers build biogeographically sensitive genetic circuits.

Synthetic neuromorphic computing in living cells

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

A hierarchy of biomolecular proportional-integral-derivative feedback controllers for robust perfect adaptation and dynamic performance

Proportional-Integral-Derivative (PID) feedback controllers are the most widely used controllers in industry. Recently, the design of molecular PID-controllers has been identified as an important goal for synthetic biology and the field of cybergenetics. In this paper, we consider the realization of PID-controllers via biomolecular reactions. We propose an array of topologies offering a compromise between simplicity and high performance. We first demonstrate that different biomolecular PI-controllers exhibit different performance-enhancing capabilities. Next, we introduce several derivative controllers based on incoherent feedforward loops acting in a feedback configuration. Alternatively, we show that differentiators can be realized by placing molecular integrators in a negative feedback loop, which can be augmented by PI-components to yield PID-controllers. We demonstrate that PID-controllers can enhance stability and dynamic performance, and can also reduce stochastic noise. Finally, we provide an experimental demonstration using a hybrid setup where in silico PID-controllers regulate a genetic circuit in single yeast cells.

A Primed Subpopulation of Bacteria Enables Rapid Expression of the Type 3 Secretion System in Pseudomonas aeruginosa

Type 3 secretion systems (T3SS) are complex nanomachines that span the cell envelope and play a central role in the biology of Gram-negative pathogens and symbionts. In Pseudomonas aeruginosa, T3SS expression is strongly associated with human disease severity and with mortality in murine acute pneumonia models. Uniform exposure of isogenic cells to T3SS-activating signal results in heterogeneous expression of this critical virulence trait. To understand the function of such diversity, we measured the production of the T3SS master regulator ExsA and the expression of T3SS genes using fluorescent reporters. We found that heterogeneous expression of ExsA in the absence of activating signal generates a "primed" subpopulation of cells that can rapidly induce T3SS gene expression in response to signal. T3SS expression is accompanied by a reproductive trade-off as measured by increased division time of T3SS-expressing cells. Although T3SS-primed cells are a minority of the population, they compose the majority of T3SS-expressing cells for several hours following activation. The primed state therefore allows P. aeruginosa to maximize reproductive fitness while maintaining the capacity to quickly express the T3SS. As T3SS effectors can serve as shared public goods for nonproducing cells, this division of labor benefits the population as a whole. IMPORTANCE The expression of specific virulence traits is strongly associated with Pseudomonas aeruginosa's success in establishing acute infections but is thought to carry a cost for bacteria. Producing multiprotein secretion systems or motility organelles is metabolically expensive and can target a cell for recognition by innate immune system receptors that recognize structural components of the type 3 secretion system (T3SS) or flagellum. These acute virulence factors are also negatively selected when P. aeruginosa establishes chronic infections in the lung. We demonstrate a regulatory mechanism by which only a minority subpopulation of genetically identical P. aeruginosa cells is "primed" to respond to signals that turn on T3SS expression. This phenotypic heterogeneity allows the population to maximize the benefit of rapid T3SS effector production while maintaining a rapidly growing and nonexpressing reservoir of cells that perpetuates this genotype within the population.

Model-guided design of mammalian genetic programs

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

Regulatory control of the Streptococcus mutans HdrRM LytTR Regulatory System functions via a membrane sequestration mechanism

Bacteria sense and respond to environmental changes via several broad categories of sensory signal transduction systems. Recently, we described the key features of a previously unrecognized, but widely conserved class of prokaryotic sensory system that we refer to as the LytTR Regulatory System (LRS). Our previous studies suggest that most, if not all, prokaryotic LRS membrane proteins serve as inhibitors of their cognate transcription regulators, but the inhibitory mechanisms employed have thus far remained a mystery. Using the Streptococcus mutans HdrRM LRS as a model, we demonstrate how the LRS membrane protein HdrM inhibits its cognate transcription regulator HdrR by tightly sequestering HdrR in a membrane-localized heteromeric HdrR/M complex. Membrane sequestration of HdrR prevents the positive feedback autoregulatory function of HdrR, thereby maintaining a low basal expression of the hdrRM operon. However, this mechanism can be antagonized by ectopically expressing a competitive inhibitor mutant form of HdrR that lacks its DNA binding ability while still retaining its HdrM interaction. Our results indicate that sequestration of HdrR is likely to be the only mechanism required to inhibit its transcription regulator function, suggesting that endogenous activation of the HdrRM LRS is probably achieved through a modulation of the HdrR/M interaction.

Dynamical model fitting to a synthetic positive feedback circuit in E. coli

Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single-equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time-series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.

Two-Component Biosensors: Unveiling the Mechanisms of Predictable Tunability

Many studies have been devoted to the engineering of cellular biosensors by exploiting intrinsic natural sensors. However, biosensors rely not only on input detection but also on an adequate response range. It is therefore often necessary to tune natural systems to meet the demands of specific applications in a predictable manner. In this study, we explored the customizability of two-component bacterial biosensors by modulating the main biosensor component, i.e., the receptor protein. We developed a mathematical model that describes the functional relationship between receptor abundance and activation threshold, sensitivity, dynamic range, and operating range. The defined mathematical framework allows the design of the genetic architecture of a two-component biosensor that can perform as required with minimal genetic engineering. To experimentally validate the model and its predictions, a library of biosensors was constructed. The good agreement between theoretical designs and experimental results indicates that modulation of receptor protein abundance allows optimization of biosensor designs with minimal genetic engineering.

Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation

BACKGROUND

Cellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network.

RESULTS

We find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory.

CONCLUSIONS

Our work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity when designing synthetic circuits with memory function.

Hysteretic Genetic Circuit for Detection of Proteasomal Degradation in Mammalian Cells

Synthetic hysteretic mammalian gene circuits generating sustained cellular responses to transient perturbations provide important tools to investigate complex cellular behaviors and reprogram cells for a variety of applications, ranging from protein production to cell fate decisions. The design rules of synthetic gene circuits with controlled hysteretic behaviors, however, remain uncharacterized. To identify the criteria for achieving predictable control of hysteresis, we built a genetic circuit for detection of proteasomal degradation (Hys-Deg). The Hys-Deg circuit is based on a tetracycline-controlled transactivator (tTA) variant engineered to interface with the ubiquitin proteasome system (UPS). The tTA variant activates its own expression, generating a positive feedback loop that is triggered by expression of another tTA gene that is constitutively regulated. Guided by predictive modeling, we characterized the hysteretic response of the Hys-Deg circuit. We demonstrated that control of the hysteretic response is achieved by modulating the ratio of expression of constitutive to inducible tTA. We also showed that the system can be finely tuned through dosage of the inducer tetracycline to calibrate the circuit for detection of the desired levels of UPS activation. This study establishes the design rules for building a hysteretic genetic circuit with an autoregulatory feedback loop and provides a synthetic memory module that could be easily integrated into regulatory gene networks to study and engineer complex cellular behaviors.

Modulating Responses of Toehold Switches by an Inhibitory Hairpin

The toehold switch consists of a cis-repressing switch RNA hairpin and a trans-acting trigger RNA. The binding of the trigger RNA to an unpaired toehold sequence of the switch hairpin allows for a branch migration process, exposing the start codon and ribosome binding site for translation initiation. In this work, we demonstrate that responses of toehold switches can be modulated by introducing an inhibitory hairpin that shortens the length of the unpaired toehold region. First, we investigated the effect of the toehold region length on output gene expression and showed that the second trigger RNA, which binds to the inhibitory hairpin, is necessary for output gene activation when the hairpin-to-hairpin spacing is short. Second, the apparent Hill coefficient was found generally to increase with decreasing hairpin-to-hairpin spacing or increasing hairpin number. This work expands the utility of toehold switches by providing a new way to modulate their response.

Cellular checkpoint control using programmable sequential logic

Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.

An Orthogonal Multi-input Integration System to Control Gene Expression in Escherichia coli

In many biotechnological applications, it is useful for gene expression to be regulated by multiple signals, as this allows the programming of complex behavior. Here we implement, in Escherichia coli, a system that compares the concentration of two signal molecules, and tunes GFP expression proportionally to their relative abundance. The computation is performed via molecular titration between an orthogonal σ factor and its cognate anti-σ factor. We use mathematical modeling and experiments to show that the computation system is predictable and able to adapt GFP expression dynamically to a wide range of combinations of the two signals, and our model qualitatively captures most of these behaviors. We also demonstrate in silico the practical applicability of the system as a reference-comparator, which compares an intrinsic signal (reflecting the state of the system) with an extrinsic signal (reflecting the desired reference state) in a multicellular feedback control strategy.

Dynamics of sequestration-based gene regulatory cascades

Gene regulatory cascades are ubiquitous in biology. Because regulatory cascades are integrated within complex networks, their quantitative analysis is challenging in native systems. Synthetic biologists have gained quantitative insights into the properties of regulatory cascades by building simple circuits, but sequestration-based regulatory cascades remain relatively unexplored. Particularly, it remains unclear how the cascade components collectively control the output dynamics. Here, we report the construction and quantitative analysis of the longest sequestration-based cascade in Escherichia coli. This cascade consists of four Pseudomonas aeruginosa protein regulators (ExsADCE) that sequester their partner. Our computational analysis showed that the output dynamics are controlled in a complex way by the concentration of the unbounded transcriptional activator ExsA. By systematically varying the cascade length and the synthesis rate of each regulator, we experimentally verified the computational prediction that ExsC plays a role in rapid circuit responses by sequestering the anti-activator ExsD, while ExsD increases response times by decreasing the free ExsA concentration. In contrast, when additional ExsD was introduced to the cascade via indirect negative feedback, the response time was significantly reduced. Sequestration-based regulatory cascades with negative feedback are often found in biology, and thus our finding provides insights into the dynamics of this recurring motif.

Decoupling Resource-Coupled Gene Expression in Living Cells

Synthetic biology aspires to develop frameworks that enable the construction of complex and reliable gene networks with predictable functionalities. A key limitation is that increasing network complexity increases the demand for cellular resources, potentially causing resource-associated interference among noninteracting circuits. Although recent studies have shown the effects of resource competition on circuit behaviors, mechanisms that decouple such interference remain unclear. Here, we constructed three systems in Escherichia coli, each consisting of two independent circuit modules where the complexity of one module (Circuit 2) was systematically increased while the other (Circuit 1) remained identical. By varying the expression level of Circuit 1 and measuring its effect on the expression level of Circuit 2, we demonstrated computationally and experimentally that indirect coupling between these seemingly unconnected genetic circuits can occur in three different regulatory topologies. More importantly, we experimentally verified the computational prediction that negative feedback can significantly reduce resource-coupled interference in regulatory circuits. Our results reveal a design principle that enables cells to reliably multitask while tightly controlling cellular resources.

Mathematical comparison of memory functions between mutual activation and repression networks in a stochastic environment

Biological memory is a ubiquitous function that can generate a sustained response to a transient inductive stimulus. To better understand this function, we must consider the mechanisms by which different structures of genetic networks achieve memory. Here, we investigated two competitive gene regulatory network models: the regulated mutual activation network (MAN) and the regulated mutual repression network (MRN). Stochasticity deteriorated the persistence of memory of both the MAN and the MRN. Mathematical comparison by simulation and theoretical analysis identified functional differences in the stochastic memory between the competitive models: specifically, the MAN provided much more robust, persistent memory than the MRN. The stochastic persistent memory pattern of the MAN can be adjusted by changing the binding strength of the activators, whereas the MRN required highly cooperative and strong binding repressors for robust memory.

Development of Design Rules for Reliable Antisense RNA Behavior in E. coli

A key driver of synthetic biology is the development of designable genetic parts with predictable behaviors that can be quickly implemented in complex genetic systems. However, the intrinsic complexity of gene regulation can make the rational design of genetic parts challenging. This challenge is apparent in the design of antisense RNA (asRNA) regulators. Though asRNAs are well-known regulators, the literature governing their design is conflicting and leaves the synthetic biology community without clear asRNA design rules. The goal of this study is to perform a comprehensive experimental characterization and statistical analysis of 121 unique asRNA regulators in order to resolve the conflicts that currently exist in the literature. asRNAs usually consist of two regions, the Hfq binding site and the target binding region (TBR). First, the behaviors of several high-performing Hfq binding sites were compared, in terms of their ability to improve repression efficiencies and their orthogonality. Next, a large-scale analysis of TBR design parameters identified asRNA length, the thermodynamics of asRNA-mRNA complex formation, and the percent of target mismatch as key parameters for TBR design. These parameters were used to develop simple asRNA design rules. Finally, these design rules were applied to construct both a simple and a complex genetic circuit containing different asRNAs, and predictable behavior was observed in both circuits. The results presented in this study will drive synthetic biology forward by providing useful design guidelines for the construction of asRNA regulators with predictable behaviors.

Splitting nodes and linking channels: A method for assembling biocircuits from stochastic elementary units

Akin to electric circuits, we construct biocircuits that are manipulated by cutting and assembling channels through which stochastic information flows. This diagrammatic manipulation allows us to create a method which constructs networks by joining building blocks selected so that (a) they cover only basic processes; (b) it is scalable to large networks; (c) the mean and variance-covariance from the Pauli master equation form a closed system; and (d) given the initial probability distribution, no special boundary conditions are necessary to solve the master equation. The method aims to help with both designing new synthetic signaling pathways and quantifying naturally existing regulatory networks.

An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability

Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.

Hypothetical biomolecular probe based on a genetic switch with tunable symmetry and stability

Background

Genetic switches are ubiquitous in nature, frequently associated with the control of cellular functions and developmental programs. In the realm of synthetic biology, it is of great interest to engineer genetic circuits that can change their mode of operation from monostable to bistable, or even to multistable, based on the experimental fine-tuning of readily accessible parameters. In order to successfully design robust, bistable synthetic circuits to be used as biomolecular probes, or understand modes of operation of such naturally occurring circuits, we must identify parameters that are key in determining their characteristics.

Results

Here, we analyze the bistability properties of a general, asymmetric genetic toggle switch based on a chemical-reaction kinetic description. By making appropriate approximations, we are able to reduce the system to two coupled differential equations. Their deterministic stability analysis and stochastic numerical simulations are in excellent agreement. Drawing upon this general framework, we develop a model of an experimentally realized asymmetric bistable genetic switch based on the LacI and TetR repressors. By varying the concentrations of two synthetic inducers, doxycycline and isopropyl β-D-1-thiogalactopyranoside, we predict that it will be possible to repeatedly fine-tune the mode of operation of this genetic switch from monostable to bistable, as well as the switching rates over many orders of magnitude, in an experimental setting. Furthermore, we find that the shape and size of the bistability region is closely connected with plasmid copy number.

Conclusions

Based on our numerical calculations of the LacI-TetR asymmetric bistable switch phase diagram, we propose a generic work-flow for developing and applying biomolecular probes: Their initial state of operation should be specified by controlling inducer concentrations, and dilution due to cellular division would turn the probes into memory devices in which information could be preserved over multiple generations. Additionally, insights from our analysis of the LacI-TetR system suggest that this particular system is readily available to be employed in this kind of probe.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0279-y) contains supplementary material, which is available to authorized users.

Programmable control of bacterial gene expression with the combined CRISPR and antisense RNA system

A central goal of synthetic biology is to implement diverse cellular functions by predictably controlling gene expression. Though research has focused more on protein regulators than RNA regulators, recent advances in our understanding of RNA folding and functions have motivated the use of RNA regulators. RNA regulators provide an advantage because they are easier to design and engineer than protein regulators, potentially have a lower burden on the cell and are highly orthogonal. Here, we combine the CRISPR system from Streptococcus pyogenes and synthetic antisense RNAs (asRNAs) in Escherichia coli strains to repress or derepress a target gene in a programmable manner. Specifically, we demonstrate for the first time that the gene target repressed by the CRISPR system can be derepressed by expressing an asRNA that sequesters a small guide RNA (sgRNA). Furthermore, we demonstrate that tunable levels of derepression can be achieved (up to 95%) by designing asRNAs that target different regions of a sgRNA and by altering the hybridization free energy of the sgRNA-asRNA complex. This new system, which we call the combined CRISPR and asRNA system, can be used to reversibly repress or derepress multiple target genes simultaneously, allowing for rational reprogramming of cellular functions.