Genetic Circuits Research Articles

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells

AbstractEngineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR‐Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA‐based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis‐related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells.

Engineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR-Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA-based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis-related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Improving Microbial Cell Factory Performance by Engineering SAM Availability.

Methylated natural products are widely spread in nature. S-Adenosyl-l-methionine (SAM) is the secondary abundant cofactor and the primary methyl donor, which confer natural products with structural and functional diversification. The increasing demand for SAM-dependent natural products (SdNPs) has motivated the development of microbial cell factories (MCFs) for sustainable and efficient SdNP production. Insufficient and unsustainable SAM availability hinders the improvement of SdNP MCF performance. From the perspective of developing MCF, this review summarized recent understanding of de novo SAM biosynthesis and its regulatory mechanism. SAM is just the methyl mediator but not the original methyl source. Effective and sustainable methyl source supply is critical for efficient SdNP production. We compared and discussed the innate and relatively less explored alternative methyl sources and identified the one involving cheap one-carbon compound as more promising. The SAM biosynthesis is synergistically regulated on multilevels and is tightly connected with ATP and NAD(P)H pools. We also covered the recent advancement of metabolic engineering in improving intracellular SAM availability and SdNP production. Dynamic regulation is a promising strategy to achieve accurate and dynamic fine-tuning of intracellular SAM pool size. Finally, we discussed the design and engineering constraints underlying construction of SAM-responsive genetic circuits and envisioned their future applications in developing SdNP MCFs.

Orthogonal LoxPsym sites allow multiplexed site-specific recombination in prokaryotic and eukaryotic hosts

Site-specific recombinases such as the Cre-LoxP system are routinely used for genome engineering in both prokaryotes and eukaryotes. Importantly, recombinases complement the CRISPR-Cas toolbox and provide the additional benefit of high-efficiency DNA editing without generating toxic DNA double-strand breaks, allowing multiple recombination events at the same time. However, only a handful of independent, orthogonal recombination systems are available, limiting their use in more complex applications that require multiple specific recombination events, such as metabolic engineering and genetic circuits. To address this shortcoming, we develop 63 symmetrical LoxP variants and test 1192 pairwise combinations to determine their cross-reactivity and specificity upon Cre activation. Ultimately, we establish a set of 16 orthogonal LoxPsym variants and demonstrate their use for multiplexed genome engineering in both prokaryotes (E. coli) and eukaryotes (S. cerevisiae and Z. mays). Together, this work yields a significant expansion of the Cre-LoxP toolbox for genome editing, metabolic engineering and other controlled recombination events, and provides insights into the Cre-LoxP recombination process.

Engineering mammalian cell growth dynamics for biomanufacturing

Precise control over mammalian cell growth dynamics poses a major challenge in biopharmaceutical manufacturing. Here, we present a multi-level cell engineering strategy for the tunable regulation of growth phases in mammalian cells. Initially, we engineered mammalian death phase by employing CRISPR/Cas9 to knockout pro-apoptotic proteins Bax and Bak, resulting in a substantial attenuation of apoptosis by improving cell viability and extending culture lifespan. The second phase introduced a growth acceleration system, akin to a “gas pedal”, based on an abscidic acid inducible system regulating cMYC gene expression, enabling rapid cell density increase and cell cycle control. The third phase focused on a stationary phase inducing system, comparable to a “brake pedal”. A tetracycline inducible genetic circuit based on BLIMP1 gene led to cell growth cessation and arrested cell cycle upon activation. Finally, we developed a dual controllable system, combining the “gas and brake pedals”, enabling for dynamic and precise orchestration of mammalian cell growth dynamics. This work exemplifies the application of synthetic biology tools and combinatorial cell engineering, offering a sophisticated framework for manipulating mammalian cell growth and providing a unique paradigm for reprogramming cell behaviour for enhancing biopharmaceutical manufacturing and further biomedical applications.

An integrative database and its application for plant synthetic biology research

Plant synthetic biology research requires diverse bioparts that facilitate the redesign and construction of new-to-nature biological devices or systems in plants. Limited by few well-characterized bioparts for plant chassis, the development of plant synthetic biology lags behind that of its microbial counterpart. Here, we constructed a web-based Plant Synthetic BioDatabase (PSBD), which currently categorizes 1677 catalytic bioparts and 384 regulatory elements and provides information on 309 species and 850 chemicals. Online bioinformatics tools including local BLAST, chem similarity, phylogenetic analysis, and visual strength are provided to assist with the rational design of genetic circuits for manipulation of gene expression in planta. We demonstrated the utility of the PSBD by functionally characterizing taxadiene synthase 2 and its quantitative regulation in tobacco leaves. More powerful synthetic devices were then assembled to amplify the transcriptional signals, enabling enhanced expression of flavivirus non-structure 1 proteins in plants. The PSBD is expected to be an integrative and user-centered platform that provides a one-stop service for diverse applications in plant synthetic biology research.

A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice

Realizing genetic circuits on single DNA molecules as self-encoded dissipative nanodevices is a major step toward miniaturization of autonomous biological systems. A circuit operating on a single DNA implies that genetically encoded proteins localize during coupled transcription-translation to DNA, but a single-molecule measurement demonstrating this has remained a challenge. Here, we use a genetically encoded fluorescent reporter system with improved temporal resolution and observe the synthesis of individual proteins tethered to a DNA molecule by transient complexes of RNA polymerase, messenger RNA, and ribosome. Against expectations in dilute cell-free conditions where equilibrium considerations favor dispersion, these nascent proteins linger long enough to regulate cascaded reactions on the same DNA. We rationally design a pulsatile genetic circuit by encoding an activator and repressor in feedback on the same DNA molecule. Driven by the local synthesis of only several proteins per hour and gene, the circuit dynamics exhibit enhanced variability between individual DNA molecules, and fluctuations with a broad power spectrum. Our results demonstrate that co-expressional localization, as a nonequilibrium process, facilitates single-DNA genetic circuits as dissipative nanodevices, with implications for nanobiotechnology applications and artificial cell design.

Enhancing tumor-specific recognition of programmable synthetic bacterial consortium for precision therapy of colorectal cancer

Probiotics hold promise as a potential therapy for colorectal cancer (CRC), but encounter obstacles related to tumor specificity, drug penetration, and dosage adjustability. In this study, genetic circuits based on the E. coli Nissle 1917 (EcN) chassis were developed to sense indicators of tumor microenvironment and control the expression of therapeutic payloads. Integration of XOR gate amplify gene switch into EcN biosensors resulted in a 1.8-2.3-fold increase in signal output, as confirmed by mathematical model fitting. Co-culturing programmable EcNs with CRC cells demonstrated a significant reduction in cellular viability ranging from 30% to 50%. This approach was further validated in a mouse subcutaneous tumor model, revealing 47%-52% inhibition of tumor growth upon administration of therapeutic strains. Additionally, in a mouse tumorigenesis model induced by AOM and DSS, the use of synthetic bacterial consortium (SynCon) equipped with multiple sensing modules led to approximately 1.2-fold increased colon length and 2.4-fold decreased polyp count. Gut microbiota analysis suggested that SynCon maintained the abundance of butyrate-producing bacteria Lactobacillaceae NK4A136, whereas reducing the level of gut inflammation-related bacteria Bacteroides. Taken together, engineered EcNs confer the advantage of specific recognition of CRC, while SynCon serves to augment the synergistic effect of this approach.

Application of (bio) chemical engineering concepts and tools to model genetic regulatory circuits, and some essential central carbon metabolism pathways in living cells. Part 4. Applications in the design of some Genetically Modified Micro-Organisms (GMOs)

In the first part of this work, the general Chemical and Biochemical Engineering (CBE) concepts and rules are briefly reviewed, together with the rules of the control theory of Nonlinear Systems (NSCT), all in the context of (i) deriving deterministic Modular Structured Kinetic Models (MSDKM) to describe the dynamics of metabolic processes in living cells, and (ii) of Hybrid Structured Modular Dynamic Models (HSMDM) (with continuous variables, linking the cell-nano-scale MSDKM state variables to the macro-scale state variables of the bioreactor dynamic model). Thus, in the HSMDM model, both prediction quality and its validity range are improved. By contrast, the current (classical/default) approach in bioengineering practice for solving design, optimization, and control problems based on the math models of industrial biological reactors is to use unstructured Monod (for cell culture reactor) or simple Michaelis-Menten (if only enzymatic reactions are retained) global kinetic models by ignoring detailed representations of metabolic cellular processes. By contrast, as reviewed, and exemplified in the second part of this work, an accurate and realistic math modelling of the dynamic individual GERMs (gene expression regulatory module), or genetic regulatory circuits (GRC), and cell-scale CCM (central carbon metabolism) key-modules can be done by only using the novel holistic ’Whole-Cell Of Variable-Volume’ (WCVV) modelling framework, under isotonic/homeostatic conditions/constraints introduced and promoted by the author. An example was given in the same Part 2 for the case study of a dynamic model for the oscillating glycolysis coupled with the Tryptophan (TRP) oscillating synthesis in the E. coli cells.

The naringenin-dependent regulator FdeR can be applied as a NIMPLY gate controlled by naringenin and arabinose.

The FdeR regulator has been reported as a transcriptional activator dependent on the interaction with naringenin. Previously, FdeR and its cognate promoter were used to construct naringenin-sensitive sensors, though no correlation was associated between the FdeR level of expression and outputs. Therefore, to understand this correlation, we constructed a circuit with FdeR expression adjusted by the arabinose concentration through an AraC-PBAD system and the FdeR-regulated promoter controlling the expression of GFP. We observed a significant reduction in the activity of the target promoter by increasing FdeR expression, indicating that although FdeR has been primarily classified as a transcriptional activator, it also represses transcription. Leveraging the bifunctional feature of FdeR, acting as both transcriptional activator and repressor, we demonstrated that this genetic circuit, when previously switched on by naringenin, can be switched off by inducing an increased FdeR expression level. This engineered system functioned as a NIMPLY gate, effectively decreasing GFP expression by 50% when arabinose was added without removing naringenin from the medium. Exploiting FdeR versatility, this study demonstrates an innovative application of this transcriptional factor for developing novel NIMPLY gates activated by a molecule with low toxicity and nutraceutical properties that may be important for several applications. Graphical Abstract.

Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917.

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Arsenic bioremediation in mining wastewater by controllable genetically modified bacteria with biochar

Arsenic (As) is a highly toxic chemical element associated with cardiovascular diseases and diabetes. Using genetically modified microorganisms (GMOs) for arsenic detoxification or removal is taking center stage in recent years. However, the bio-safety issue of GMOs remains an obstacle. In this study, we proposed a bioremediation process to efficiently adsorb arsenic from mining wastewater by combining a controllable GMO with biochar. One metagenomic gene element (ArsR) with high arsenic adsorption efficiency was newly identified from mining wastewater environment. Then the gene was chromosomally integrated into strain Pseudomonas putida KT2440, which is a widely used strain for environmental bioremediation. The growth-regulation genetic circuit coordinating suicide gene expression was deployed onto the genome of strain KT2440. This non-auxotrophic, antibiotic-free and self-controlled programmable circuit was fine-tuned with highly sensitive perceptibility towards arsenic levels below wastewater discharge standard (< 0.50 mg/L), which made the system applicable. A biochar-microorganism coupled recovery strategy was further implemented in mining wastewater treatment, leading to increased arsenic removal capacities, which is ready for use under a circumstance up to 20 mg/L of total arsenic pollution. No more than 1.32 × 10−9 escapee of GMOs was observed in the process, which meets the US NIH guidelines for GMOs release (<10−8). Thus, this study demonstrated the feasibility for arsenic bioremediation by GMOs in industrial wastewater.

Self-feedback loop-containing synthetic mRNA switches for controlled microRNA sensing

Synthetic mRNA switches are powerful cell fate manipulation tools that sense cellular input molecules to directly control protein expression at the translational level. The lack of available switch designs that can mimic the natural sophisticated protein regulation is a fundamental issue that limits the application of synthetic mRNA switches. Here we report a new set of synthetic mRNA switches by incorporating self-feedback loop machineries to dynamically control protein expression levels upon sensing cellular microRNAs. We redesigned the coding region of the switch to express output protein along with mRNA regulatory proteins. RNA-binding proteins (RBPs) and RBP-binding RNA motifs (aptamers) guide the regulatory proteins to act on their own mRNAs, enhancing or flattening the effect of microRNA sensing. Importantly, we demonstrated that the switches with the positive feedback feature can enlarge a high-or-low microRNA effect into a nearly all-or-none pattern, substantially boosting the use of synthetic mRNA switches as high-performance microRNA sensors or binary cell regulation tools. We believe these novel mRNA switch designs provide new strategies to construct complex mRNA-based genetic circuits for future molecular sensing and cell engineering.

Modularized Design and Construction of Tunable Microbial Consortia with Flexible Topologies.

Complex and fluid bacterial community compositions are critical to diversity, stability, and function. However, quantitative and mechanistic descriptions of the dynamics of such compositions are still lacking. Here, we develop a modularized design framework that allows for bottom-up construction and the study of synthetic bacterial consortia with different topologies. We showcase the microbial consortia design and building process by constructing amensalism and competition consortia using only genetic circuit modules to engineer different strains to form the community. Functions of modules and hosting strains are validated and quantified to calibrate dynamic parameters, which are then directly fed into a full mechanistic model to accurately predict consortia composition dynamics for both amensalism and competition without further fitting. More importantly, such quantitative understanding successfully identifies the experimental conditions to achieve coexistence composition dynamics. These results illustrate the process of both computationally and experimentally building up bacteria consortia complexity and hence achieve robust control of such fluid systems.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization.

Flapjack presents a valuable solution for addressing challenges in the Design, Build, Test, Learn (DBTL) cycle of engineering synthetic genetic circuits. This platform provides a comprehensive suite of features for managing, analyzing, and visualizing kinetic gene expression data and associated metadata. By utilizing the Flapjack platform, researchers can effectively integrate the test phase with the build and learn phases, facilitating the characterization and optimization of genetic circuits. With its user-friendly interface and compatibility with external software, the Flapjack platform offers a practical tool for advancing synthetic biology research.This chapter provides an overview of the data model employed in Flapjack and its hierarchical structure, which aligns with the typical steps involved in conducting experiments and facilitating intuitive data management for users. Additionally, this chapter offers a detailed description of the user interface, guiding readers through accessing Flapjack, navigating its sections, performing essential tasks such as uploading data and creating plots, and accessing the platform through the pyFlapjack Python package.

A MoClo-Compatible Toolbox of ECF Sigma Factor-Based Regulatory Switches for Proteobacterial Chassis.

The construction of complex synthetic gene circuits with predetermined and reliable output depends on orthogonal regulatory parts that do not inadvertently interfere with the host machinery or with other circuit components. Previously, extracytoplasmic function sigma factors (ECFs), a diverse group of alternative sigma factors with distinct promoter specificities, were shown to have great potential as context-independent regulators, but so far, they have only been used in a few model species. Here, we show that the alphaproteobacterium Sinorhizobium meliloti, which has been proposed as a plant-associated bacterial chassis for synthetic biology, has a similar phylogenetic ECF acceptance range as the gammaproteobacterium Escherichia coli. A common set of orthogonal ECF-based regulators that can be used in both bacterial hosts was identified and used to create 2-step delay circuits. The genetic circuits were implemented in single copy in E. coli by chromosomal integration using an established method that utilizes bacteriophage integrases. In S. meliloti, we demonstrated the usability of single-copy pABC plasmids as equivalent carriers of the synthetic circuits. The circuits were either implemented on a single pABC or modularly distributed on 3 such plasmids. In addition, we provide a toolbox containing pABC plasmids compatible with the Golden Gate (MoClo) cloning standard and a library of basic parts that enable the construction of ECF-based circuits in S. meliloti and in E. coli. This work contributes to building a context-independent and species-overarching ECF-based toolbox for synthetic biology applications.

Genetic Engineering of Microorganisms with Electroactive Genes for the Fabrication of Electrochemical Microbial Biosensors.

By integrating electroactive genes into engineered sensing microorganisms, information about the object to be measured can be converted into the output of an electrical signal, omitting the process of converting the output of an electrical signal in conventional sensing strategies and simplifying the steps of biosensor development. By utilizing synthetic biology methods, we can not only create novel genetic circuits by using logic gate operations and integrating genes from other biological components, solving biosensing issues in living systems and enhancing sensor performance, but also convert various types of genetic circuits into electrical signals, broadening the application range of biosensors. Here, we describe an example of how to genetically engineer microorganisms with electroactive genes and the fabrication of an electrochemical microbial biosensor.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors.

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Mini-bioreactor for parts characterization of a biological circuit: Absorbance and Fluorescence measurement calibration, and online growth rate estimation

Synthetic Biology, like many other disciplines, is progressing every day. This progress has created the necessity to develop devices for measuring parameters and variables in order to characterize different aspects of genetic circuits. In the market, we can find many devices that fulfill this purpose. One of these devices is Chi.Bio, an open-source mini-bioreactor platform. The platform Chi.Bio allows us to measure process variables, such as fluorescence and absorbance, using its different sensors. Thus, it is necessary to develop protocols and procedures for calibrating data measurements from the Chi.Bio to increase the usability and interpretability of models and estimated parameters of a genetic circuit. Here, we propose a set of protocols and calibration procedures to obtain measurements of fluorescent E. coli cells in Molecules of Equivalent Fluorescein (MEFL) per cell for GFP constructs and we present an implementation of a second-order sliding mode observer to estimate the growth rate of E. coli cells in batch mode of Chi.Bio. The results show that calibrated measures are fundamental for getting reliable values to characterize genetic circuits in Synthetic Biology.

Learning mechanism for a collective classifier based on competition driven by training examples

The purpose of this work is to modify the learning mechanism of a collective classifier in order to provide learning by population dynamics alone, without requiring an external sorting device. A collective classifier is an ensemble of non-identical simple elements, which do not have any intrinsic dynamics neither variable parameters; the classifier admits learning by adjusting the composition of the ensemble, which was provided in the preceding literature by selecting the ensemble elements using a sorting device. Methods. The population dynamics model of a collective classifier is extended by adding a “learning subsystem”, which is controlled by a sequence of training examples and, in turn, controls the strength of intraspecific competition in the population dynamics. The learning subsystem dynamics is reduced to a linear mapping with random parameters expressed via training examples. The solution to the mapping is an asymptotically stationary Markovian random process, for which we analytically find asymptotic expectation and show its variance to vanish in the limit under the specified assumptions, thus allowing an approximate deterministic description of the coupled population dynamics based on available results from the preceding literature. Results. We show analytically and illustrate it by numerical simulation that the decision rule of our classifier in the course of learning converges to the Bayesian rule under assumptions which are essentially in line with available literature on collective classifiers. The implementation of the required competitive dynamics does not require an external sorting device. Conclusion. We propose a conceptual model for a collective classifier, whose learning is fully provided by its own population dynamics. We expect that our classifier, similarly to the approaches taken in the preceding literature, can be implemented as an ensemble of living cells equipped with synthetic genetic circuits, when a mechanism of population dynamics with synthetically controlled intraspecific competition becomes available.