Synthetic Gene Circuits Research Articles

A Synthetic Gene Circuit for Self-Regulating Delivery of Biologic Drugs in Engineered Tissues.

We engineered a synthetic transcription system based on nuclear factor kappa-light-chain-enhancer of activated B cells signaling that can attenuate the effects of the inflammatory cytokine interleukin (IL)-1α in a self-regulating manner. This system responds in a time- and dose-dependent manner to rapidly produce therapeutic levels of IL-1 receptor antagonist (IL-1Ra). The use of lentiviral gene therapy allows this system to be utilized through different transduction methods and in different cell types for a variety of applications. Broadly, this approach may be applicable in developing autoregulated biologic systems for tissue engineering and drug delivery in a range of disease applications.

Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies.

Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.

Rapid screening of engineered microbial therapies in a 3D multicellular model

Synthetic biology is transforming therapeutic paradigms by engineering living cells and microbes to intelligently sense and respond to diseases including inflammation, infections, metabolic disorders, and cancer. However, the ability to rapidly engineer new therapies far outpaces the throughput of animal-based testing regimes, creating a major bottleneck for clinical translation. In vitro approaches to address this challenge have been limited in scalability and broad applicability. Here, we present a bacteria-in-spheroid coculture (BSCC) platform that simultaneously tests host species, therapeutic payloads, and synthetic gene circuits of engineered bacteria within multicellular spheroids over a timescale of weeks. Long-term monitoring of bacterial dynamics and disease progression enables quantitative comparison of critical therapeutic parameters such as efficacy and biocontainment. Specifically, we screen Salmonella typhimurium strains expressing and delivering a library of antitumor therapeutic molecules via several synthetic gene circuits. We identify candidates exhibiting significant tumor reduction and demonstrate high similarity in their efficacies, using a syngeneic mouse model. Last, we show that our platform can be expanded to dynamically profile diverse microbial species including Listeria monocytogenes, Proteus mirabilis, and Escherichia coli in various host cell types. This high-throughput framework may serve to accelerate synthetic biology for clinical applications and for understanding the host-microbe interactions in disease sites.

Sharing Resources Can Lead to Monostability in a Network of Bistable Toggle Switches

The vision of synthetic biology is to engineer complex cellular systems in a modular fashion. Unfortunately, multiple factors hinder such modular design, thus the scalability of rationally engineering synthetic gene circuits. One major barrier emerges due to the limited availability of shared cellular resources, intertwining the behavior of otherwise disconnected components. It was recently demonstrated that these unwanted and pervasive coupling effects can be accurately predicted both in vitro and in vivo . Here, we not only analyze how these effects influence the collective behavior of multiple toggle switches but we also illustrate how to leverage this predominantly disadvantageous coupling. In particular, we prove that by turning on/off a load module, the network of toggle switches can be switched between monostable and multistable modes. Importantly, this can be achieved without any direct regulatory connection among the toggle switches or between the load module and any of the toggle switches, thus presenting a major experimental advantage over relying on traditional regulatory linkages. This idea of exploiting and leveraging resource competition is illustrated through the design of a cellular random number generator.

Revisiting a synthetic intracellular regulatory network that exhibits oscillations.

In 2000, Elowitz and Leibler introduced the repressilator-a synthetic gene circuit with three genes that cyclically repress transcription of the next gene-as well as a corresponding mathematical model. Experimental data and model simulations exhibited oscillations in the protein concentrations across generations. Müller et al. (J Math Biol 53(6):905-937, 2006) generalized the model to an arbitrary number of genes and analyzed the resulting dynamics. Their new model arose from five key assumptions, two of which are restrictive given current biological knowledge. Accordingly, we propose a new repressilator system that allows for general functions to model transcription, degradation, and translation. We prove that, with an odd number of genes, the new model has a unique steady state and the system converges to this steady state or to a periodic orbit. We also give a necessary and sufficient condition for stability of steady states when the number of genes is even and conjecture a condition for stability for an odd number. Finally, we derive a new rate function describing transcription that arises under more reasonable biological assumptions than the widely used single-step binding assumption. With this new transcription-rate function, we compare the model's amplitude and period with that of a model with the conventional transcription-rate function. Taken together, our results enhance our understanding of genetic regulation by repression.

A frame-shifted gene, which rescued its function by non-natural start codons and its application in constructing synthetic gene circuits

BackgroundFrame-shifted genes results in non-functional peptides. Because of this complete loss of function, frame-shifted genes have never been used in constructing synthetic gene circuits.ResultsHere we report that the function of gene circuits is rescued by a frame-shifted gene, which functions by translating from a non-natural start codon. We report a single nucleotide deletion mutation that developed in the λ-repressor cI within a synthetic genetic NOT gate in Escherichia coli during growth and through this mutation, a non-functional synthetic gene circuit became functional. This mutation resulted in a frame-shifted cI, which showed effective functionality among genetic NOT-gates in Escherichia coli with high regulatory ranges (> 300) and Hill coefficient (> 6.5). The cI worked over a large range of relative copy numbers between the frame-shifted gene and its target promoter. These properties make this frame-shifted gene an excellent candidate for building synthetic gene circuits. We hypothesized a new operating mechanism and showed evidence that frame-shifted cI was translated from non-natural start codon. We have engineered and tested a series of NOT gates made from a library of cI genes, each of which starts from a different codon within the first several amino acids of the frame-shifted cI. It is found that one form with start codon ACA, starting from the 3rd codon had similar repression behavior as the whole frame-shifted gene. We demonstrated synthetic genetic NAND and NOR logic-gates with frame-shifted cI. This is the first report of synthetic-gene-circuits made from a frame-shifted gene.ConclusionsThis study inspires a new view on frame-shifted gene and may serve as a novel way of building and optimizing synthetic-gene-circuits. This work may also have significance in the understanding of non-directed evolution of synthetic genetic circuits.

3352 Surgical Adjuvant of Immunomodulatory Gene Circuits for Treatment of Glioblastoma

OBJECTIVES/SPECIFIC AIMS: Glioblastoma (GBM) is a brain cancer with a devastatingly short overall survival of under two years. The poor prognosis of GBM is largely due to cell invasion and maintenance of cancer initiating cells that evade the brain’s innate and adaptive immune responses which enables escape from surgical resection and drives inevitable recurrence. While targeting the brain’s immune microenvironment has long been proposed as a strategy for treating GBM, translational progress has been slow, underscoring the need to investigate the brain’s immune microenvironment for therapeutic avenues. METHODS/STUDY POPULATION: Recent advancements in tunable synthetic immunomodulatory gene circuits targeting metastatic cancers has demonstrated the novel ability to use engineering principles to induce infiltrative cancer cells to express combinatorial immunomodulatory outputs that enable T-cell killing4. Our central hypothesis is: we will be able to significantly improve survival with a lasting immune-mediated control of GBM by using synthetic immunomodulatory gene circuits driving GBM cells to express a local combination of immunomodulatory proteins: human IL15, a surface T-cell engager, PD-L1-CD3 bispecific antibody, and the protein, LIGHT (TNFRSF14). Importantly, the co-expression of LIGHT and anti-PD-L1 therapies was recently shown to rescue PD-L1 checkpoint blockage in the preclinical models of brain tumors and significant enhance survival outcomes highlighting the benefits of novel combinations of immunomodulatory proteins for treatment of GBM. To identify genes whose expression is dramatically upregulated in GBM compared to normal human brain cells, a pooled of six thousand lentiviral oncogene promoters that drive expression of a red-fluorescent protein has been infected into three human GBM cell lines. RESULTS/ANTICIPATED RESULTS: We have successfully infected our GBM cells and are preparing samples for next generation DNA sequencing to determine highly active promoters in GBM that are not expressed in multiple normal brain cells types, astrocytes and neurons. These chosen promoters will then be used to drive an AND gate logic gene circuit immunotherapy outputs which is currently under development for both in-vitro and in-vivo experiments. DISCUSSION/SIGNIFICANCE OF IMPACT: We anticipate that local expression of multiple immune effectors proteins will significantly enhance tumor control and survival in both synergistic murine and human-murine xenograft pre-clinical models of GBM. Ultimately, our goal is to rapidly translate this technology advance into the clinical trial for adult GBM patients.

Translational Control using an Expanded Genetic Code.

A bio-orthogonal and unnatural substance, such as an unnatural amino acid (Uaa), is an ideal regulator to control target gene expression in a synthetic gene circuit. Genetic code expansion technology has achieved Uaa incorporation into ribosomal synthesized proteins in vivo at specific sites designated by UAG stop codons. This site-specific Uaa incorporation can be used as a controller of target gene expression at the translational level by conditional read-through of internal UAG stop codons. Recent advances in optimization of site-specific Uaa incorporation for translational regulation have enabled more precise control over a wide range of novel important applications, such as Uaa-auxotrophy-based biological containment, live-attenuated vaccine, and high-yield zero-leakage expression systems, in which Uaa translational control is exclusively used as an essential genetic element. This review summarizes the history and recent advance of the translational control by conditional stop codon read-through, especially focusing on the methods using the site-specific Uaa incorporation.

Model mismatch in multi-objective optimisation and preservation of trade-off order.

Multi-objective optimisation is a valuable tool for tuning dynamical systems when simultaneous optimisation performance objectives are in conflict. When the goal is tuning the parameters of a synthetic biology device, mismatch between the model implemented in silico -a more or less coarse simplification of the real system- and the actual in vivo implementation might lead to a disagreement between the in silico and in vivo design objectives for a given solution from the Pareto front. Here, we propose an iterative closed-loop multi-objective optimisation approach where the new information provided by the difference between the in silico Pareto front and its in vivo implementation is used to improve the parametric model. This aims to minimise the discrepancies between in silico and in vivo performance objectives while preserving the trade-off order among solutions. As a proof-of-concept we consider the problem of tuning a synthetic gene circuit used as feedforward-feedback controller for the expression of a protein of interest. We use an extended parametric model of the gene synthetic circuit to represent the in vivo set up and a simplified one for the in silico one.

A revolutionary tool: CRISPR technology plays an important role in construction of intelligentized gene circuits.

With the development of synthetic biology, synthetic gene circuits have shown great applied potential in medicine, biology, and as commodity chemicals. An ultimate challenge in the construction of gene circuits is the lack of effective, programmable, secure and sequence‐specific gene editing tools. The clustered regularly interspaced short palindromic repeat (CRISPR) system, a CRISPR‐associated RNA‐guided endonuclease Cas9 (CRISPR‐associated protein 9)‐targeted genome editing tool, has recently been applied in engineering gene circuits for its unique properties‐operability, high efficiency and programmability. The traditional single‐targeted therapy cannot effectively distinguish tumour cells from normal cells, and gene therapy for single targets has poor anti‐tumour effects, which severely limits the application of gene therapy. Currently, the design of gene circuits using tumour‐specific targets based on CRISPR/Cas systems provides a new way for precision cancer therapy. Hence, the application of intelligentized gene circuits based on CRISPR technology effectively guarantees the safety, efficiency and specificity of cancer therapy. Here, we assessed the use of synthetic gene circuits and if the CRISPR system could be used, especially artificial switch‐inducible Cas9, to more effectively target and treat tumour cells. Moreover, we also discussed recent advances, prospectives and underlying challenges in CRISPR‐based gene circuit development.

Encryption and steganography of synthetic gene circuits

Synthetic biologists use artificial gene circuits to control and engineer living cells. As engineered cells become increasingly commercialized, it will be desirable to protect the intellectual property contained in these circuits. Here, we introduce strategies to hide the design of synthetic gene circuits, making it more difficult for an unauthorized third party to determine circuit structure and function. We present two different approaches: the first uses encryption by overlapping uni-directional recombinase sites to scramble circuit topology and the second uses steganography by adding genes and interconnections to obscure circuit topology. We also discuss a third approach: to use synthetic genetic codes to mask the function of synthetic circuits. For each approach, we discuss relative strengths, weaknesses, and practicality of implementation, with the goal to inspire further research into this important and emerging area.

Revisiting asymptotic periodicity in networks of degrade-and-fire oscillators

Networks of degrade-and-fire oscillators are elementary models of populations of synthetic gene circuits with negative feedback, which show elaborate phenomenology while being amenable to mathematical analysis. In addition to thorough investigation in various examples of interaction graphs, previous studies have obtained conditions on interaction topology and strength that ensure that asymptotic behaviors are periodic (assuming that the so-called firing sequence is itself periodic and involves all nodes). Here, we revisit and extend these conditions and we analyse the dynamics in a case of unidirectional periodic chain. This example shows in particular that the updated conditions for asymptotic periodicity are optimal. Altogether, our results provide a novel instance of direct impact of the topology of interactions in the global dynamics of a collective system.

Arabinose-Induced Catabolite Repression as a Mechanism for Pentose Hierarchy Control in Clostridium acetobutylicum ATCC 824.

Bacterial fermentation of carbohydrates from sustainable lignocellulosic biomass into commodity chemicals by the anaerobic bacterium Clostridium acetobutylicum is a promising alternative source to fossil fuel-derived chemicals. Recently, it was demonstrated that xylose is not appreciably fermented in the presence of arabinose, revealing a hierarchy of pentose utilization in this organism (L. Aristilde, I. A. Lewis, J. O. Park, and J. D. Rabinowitz, Appl Environ Microbiol 81:1452-1462, 2015, https://doi.org/10.1128/AEM.03199-14). The goal of the current study is to characterize the transcriptional regulation that occurs and perhaps drives this pentose hierarchy. Carbohydrate consumption rates showed that arabinose, like glucose, actively represses xylose utilization in cultures fermenting xylose. Further, arabinose addition to xylose cultures led to increased acetate-to-butyrate ratios, which indicated a transition of pentose catabolism from the pentose phosphate pathway to the phosphoketolase pathway. Transcriptome sequencing (RNA-Seq) confirmed that arabinose addition to cells actively growing on xylose resulted in increased phosphoketolase (CA_C1343) mRNA levels, providing additional evidence that arabinose induces this metabolic switch. A significant overlap in differentially regulated genes after addition of arabinose or glucose suggested a common regulation mechanism. A putative open reading frame (ORF) encoding a potential catabolite repression phosphocarrier histidine protein (Crh) was identified that likely participates in the observed transcriptional regulation. These results substantiate the claim that arabinose is utilized preferentially over xylose in C. acetobutylicum and suggest that arabinose can activate carbon catabolite repression via Crh. Furthermore, they provide valuable insights into potential mechanisms for altering pentose utilization to modulate fermentation products for chemical production. IMPORTANCE Clostridium acetobutylicum can ferment a wide variety of carbohydrates to the commodity chemicals acetone, butanol, and ethanol. Recent advances in genetic engineering have expanded the chemical production repertoire of C. acetobutylicum using synthetic biology. Due to its natural properties and genetic engineering potential, this organism is a promising candidate for converting biomass-derived feedstocks containing carbohydrate mixtures to commodity chemicals via natural or engineered pathways. Understanding how this organism regulates its metabolism during growth on carbohydrate mixtures is imperative to enable control of synthetic gene circuits in order to optimize chemical production. The work presented here unveils a novel mechanism via transcriptional regulation by a predicted Crh that controls the hierarchy of carbohydrate utilization and is essential for guiding robust genetic engineering strategies for chemical production.

Multiscale effects of heating and cooling on genes and gene networks

Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

Engineering Translational Resource Allocation Controllers: Mechanistic Models, Design Guidelines, and Potential Biological Implementations.

The use of orthogonal ribosomes in combination with dynamic resource allocation controllers is a promising approach for relieving the negative effects of cellular resource limitations on the modularity of synthetic gene circuits. Here, we develop a detailed mechanistic model of gene expression and resource allocation, which when simplified to a tractable level of complexity, allows the rational design of translational resource allocation controllers. Analysis of this model reveals a fundamental design trade-off: that reducing coupling acts to decrease gene expression. Through a sensitivity analysis of the experimentally tunable controller parameters, we identify how each controller design parameter affects the overall closed-loop behavior of the system, leading to a detailed set of design guidelines for optimally managing this trade-off. On the basis of our designs, we evaluated a number of alternative potential experimental implementations of the proposed system using commonly available biological components. Finally, we show that the controller is capable of dynamically allocating ribosomes as needed to restore modularity in a number of more complex synthetic circuits, such as the repressilator, and activation cascades composed of multiple interacting modules.

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Regulating exopolysaccharide gene wcaF allows control of Escherichia coli biofilm formation

While biofilms are known to cause problems in many areas of human health and the industry, biofilms are important in a number of engineering applications including wastewater management, bioremediation, and bioproduction of valuable chemicals. However, excessive biofilm growth remains a key challenge in the use of biofilms in these applications. As certain amount of biofilm growth is required for efficient use of biofilms, the ability to control and maintain biofilms at desired thickness is vital. To this end, we developed synthetic gene circuits to control E. coli MG1655 biofilm formation by using CRISPRi/dCas9 to regulate a gene (wcaF) involved in the synthesis of colanic acid (CA), a key polysaccharide in E. coli biofilm extracellular polymeric substance (EPS). We showed that the biofilm formation was inhibited when wcaF was repressed and the biofilms could be maintained at a different thickness over a period of time. We also demonstrated that it is also possible to control the biofilm thickness spatially by inhibiting wcaF gene using a genetic light switch. The results demonstrate that the approach has great potential as a new means to control and maintain biofilm thickness in biofilm related applications.

On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.

Minimizing Context Dependency of Gene Networks Using Artificial Cells.

The functioning of synthetic gene circuits depends on their local chemical context defined by the types and concentrations of biomolecules in the surrounding milieu that influences gene transcription and translation. This chemical-context dependence of synthetic gene circuits arises from significant yet unknown cross talk between engineered components, host cells, and environmental factors and has been a persistent challenge for synthetic biology. Here, we show that the sensitivity of synthetic gene networks to their extracellular chemical contexts can be minimized, and their designed functions rendered robust using artificial cells, which are synthetic biomolecular compartments engineered from the bottom-up using liposomes that encapsulate the gene networks. Our artificial cells detect, interact with, and kill bacteria in simulated external environments with different chemical complexity. Our work enables the engineering of synthetic gene networks with minimal dependency on their extracellular chemical context and creates a new frontier in controlling robustness of synthetic biological systems using bioinspired mechanisms.

Anti-CRISPR-based biosensors in the yeast S. cerevisiae

BackgroundAnti-CRISPR proteins are expressed by phages as a reaction to the bacterial CRISPR–Cas defense system. Recently, the structures of anti-CRISPR proteins have been determined, and their diverse functions have been clarified. Anti-CRISPR proteins such as LmAcrIIA2 and LmAcrIIA4 interact with the SpCas9:gRNA system and occlude the protospacer adjacent motif (PAM) recognition site, thereby preventing SpCas9:gRNA from binding to the DNA. Hence, anti-CRISPR proteins represent a powerful means to control and modulate the activity of SpCas9 and its nuclease-deficient version dSpCas9. LmAcrIIA2 and LmAcrIIA4 have been shown to be efficient inhibitors of SpCas9 in Escherichia coli, Saccharomyces cerevisiae, and mammalian cells. To date, there have been no reports of anti-CRISPR-based synthetic gene circuits engineered into yeast cells.ResultsWe constructed in the yeast S. cerevisiae synthetic biosensors based on the anti-CRISPR–dSpCas9:gRNA interaction. Upon induction with galactose or β-estradiol, anti-CRISPR proteins (LmAcrIIA4, LmAcrIIA2, and StAcrIIA5) produced an enhancement in fluorescence expression by preventing the dSpCas9–Mxi1:gRNA complex from binding to the DNA. We found that LmAcrIIA2 performed as well as LmAcrIIA4 in S. cerevisiae, whereas StAcrIIA5, which had previously been tested in bacteria only, had non-negligible negative effects on yeast cell growth. The efficiency of anti-CRISPR-based biosensors was strongly dependent on the means by which the guide RNAs were produced. The best performance, as measured by the increase in fluorescence, was achieved using a “ribozyme–gRNA–ribozyme” expression cassette under the control of the yeast constitutive ADH1 promoter.ConclusionsThis work demonstrates that anti-CRISPR proteins are effective dSpCas9 suppressors in yeast cells. In particular, LmAcrIIA2 and LmAcrIIA4 could be employed as new components of yeast synthetic gene circuits.