Highly modular bow-tie gene circuits with programmable dynamic behaviour

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.

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

Molecular recording: transcriptional data collection into the genome

Advances in regenerative medicine depend upon understanding the complex transcriptional choreography that guides cellular development. Transcriptional molecular recorders, tools that record different transcriptional events into the genome of cells, hold promise to elucidate both the intensity and timing of transcriptional activity at single-cell resolution without requiring destructive multitime point assays. These technologies are dependent on DNA writers, which translate transcriptional signals into stable genomic mutations that encode the duration, intensity, and order of transcriptional events. In this review, we highlight recent progress toward more informative and multiplexable transcriptional recording through the use of three different types of DNA writing - recombineering, Cas1-Cas2 acquisition, and prime editing - and the architecture of the genomic data generated.

Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells

During development, cell state transitions are coordinated through changes in the identity of molecular regulators in a cell type‐ and dose‐specific manner. The ability to rationally engineer such transitions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein, we report the generation of synthetic gene circuits that can detect a desired cell state using AND‐like logic integration of endogenous miRNAs (classifiers) and, upon detection, produce fine‐tuned levels of output proteins using an miRNA‐mediated output fine‐tuning technology (miSFITs). Specifically, we created an “hPSC ON” circuit using a model‐guided miRNA selection and circuit optimization approach. The circuit demonstrates robust PSC‐specific detection and graded output protein production. Next, we used an empirical approach to create an “hPSC‐Off” circuit. This circuit was applied to regulate the secretion of endogenous BMP4 in a state‐specific and fine‐tuned manner to control the composition of differentiating hPSCs. Our work provides a platform for customized cell state‐specific control of desired physiological factors in hPSC, laying the foundation for programming cell compositions in hPSC‐derived tissues and beyond.

Neuroinflammation Following Traumatic Brain Injury: Take It Seriously or Not

Traumatic brain injury (TBI) is associated with high mortality and disability, with a substantial socioeconomic burden. With the standardization of the treatment process, there is increasing interest in the role that the secondary insult of TBI plays in outcome heterogeneity. The secondary insult is neither detrimental nor beneficial in an absolute sense, among which the inflammatory response was a complex cascade of events and can thus be regarded as a double-edged sword. Therefore, clinicians should take the generation and balance of neuroinflammation following TBI seriously. In this review, we summarize the current human and animal model studies of neuroinflammation and provide a better understanding of the inflammatory response in the different stages of TBI. In particular, advances in neuroinflammation using proteomic and transcriptomic techniques have enabled us to identify a functional specific delineation of the immune cell in TBI patients. Based on recent advances in our understanding of immune cell activation, we present the difference between diffuse axonal injury and focal brain injury. In addition, we give a figurative profiling of the general paradigm in the pre- and post-injury inflammatory settings employing a bow-tie framework.

Emergent Bistable Switches from the Incoherent Feed-Forward Signaling of a Positive Feedback Loop

Bistability is intrinsically connected to various decision making processes in living systems. The operating principles of a bistable switch, generated from a positive feedback loop, are well understood both in natural and synthetic settings. However, the fate of dynamic modularity of a positive feedback loop is unknown when it is connected to another dynamically modular signaling motif. In order to address this, here we investigate feed-forward signaling of a positive feedback loop to determine the fate of a bistable switch under such signaling. Using the potential energy based high-throughput bifurcation analysis method, we uncover that in addition to the conventional bistability the hybrid motifs generate various emergent bistable switches, namely mushroom and isola switches, which are not produced by the individual motifs. Using random parameter sampling, network perturbation, and phase plane analysis, we establish the design principles of such emergent behaviors. Incoherent feed-forward signaling of a positive feedback loop with distinct regulatory thresholds of the two arms of the feed-forward loop are the key requirements for such emergent behaviors. Our calculations show that the specific types of atypical bistable responses depend on the logic gate configuration of the signals. However, the emergent bistable behaviors of the hybrid networks do not depend on the nature of the positive feedback loop.

Context-aware synthetic biology by controller design: Engineering the mammalian cell

The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.

Multiple Alternative Promoters and Alternative Splicing Enable Universal Transcription-Based Logic Computation in Mammalian Cells

Multi-input logic gene circuits can enable sophisticated control of cell function, yet large-scale synthetic circuitry in mammalian cells has relied on post-transcriptional regulation or recombinase-triggered state transitions. Large-scale transcriptional logic, on the other hand, has been challenging to implement. Inspired by a naturally found regulatory strategy of using multiple alternative promoters, followed by alternative splicing, we developed a scalable and compact platform for transcriptional OR logic using inputs to those promoters. The platform is extended to implement disjunctive normal form (DNF) computations capable of implementing arbitrary logic rules. Specifically, AND logic is implemented at individual promoters using synergistic transcriptional inputs, and NOT logic via microRNA inputs targeting unique exon sequences driven by those promoters. Together, these regulatory programs result in DNF-like logic control of output gene expression. The approach offers flexibility for building complex logic programs in mammalian cells.

Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

One of the key features of a gene circuit is its input/output behavior. A few earlier publications attempted to develop methods to extract this behavior using transient transfection of circuit components in mammalian cells. However, the hitherto developed methods are only suitable for circuit with monomodal output distribution. Moreover, the relationship between the extracted I/O mapping and the "ground truth" that would have obtained with stably-integrated circuits, has not been addressed. Here we explore cell populations easily identifiable in flow cytometry data, namely, the peaks of fluorescent readout distribution in cells binned by the common expression value of the transfection reporter, or marker, gene. Using numerical simulations, we find that the distribution of circuit copy number in these cells deterministically depends on marker fluorescence in the noise-dependent manner. Moreover, we find that this is true also in the case of bi-modal output distribution. Using the peaks of input and output distributions, we are able to reconstruct the I/O mapping of the circuit and relate it to the I/O mapping of the stably-integrated circuit. The reconstruction is enabled by a new computational method we call PFAFF. The method is extensively validated with forward-simulated flow cytometry data from stable and transient transfections, with up to seven different circuits. The results show excellent correlation between the I/O behavior extracted by PFAFF from simulated transient transfection data, and the data simulated for stably integrated circuit.

Design of Multipartite Transcription Factors for Multiplexed Logic Genome Integration Control in Mammalian Cells

Synthetic biology relies on rapid and efficient methods to stably integrate DNA payloads encoding for synthetic biological systems into the genome of living cells. The size of designed biological systems increases with their complexity, and novel methods are needed that enable efficient and simultaneous integration of multiple payloads into single cells. By assembling natural and synthetic protein–protein dimerization domains, we have engineered a set of multipartite transcription factors for driving heterologous target gene expression. With the distribution of single parts of multipartite transcription factors on piggyback transposon-based donor plasmids, we have created a logic genome integration control (LOGIC) system that allows for efficient one-step selection of stable mammalian cell lines with up to three plasmids. LOGIC significantly enhances the efficiency of multiplexed payload integration in mammalian cells compared to traditional cotransfection and may advance cell line engineering in synthetic biology and biotechnology.

Programmable Synthetic Protein Circuits for the Identification and Suppression of Hepatocellular Carcinoma

Precisely identifying and killing tumor cells are diligent pursuits in oncotherapy. Synthesized gene circuits have emerged as an intelligent weapon to solve these problems. Gene circuits based on post-transcriptional regulation enable a faster response than systems based on transcriptional regulation, which requires transcription and translation, showing superior safety. In this study, synthetic-promoter-free gene circuits possessing two control layers were constructed to improve the specific recognition of tumor cells. Using split-TEV, we designed and verified the basic control layer of protein-protein interaction (PPI) sensing. Another orthogonal control layer was built to sense specific proteins. Two layers were integrated to generate gene circuits sensing both PPI and specific proteins, forming 10 logic gates. To demonstrate the utility of this system, the circuit was engineered to sense alpha-fetoprotein (AFP) expression and the PPI between YAP and 14-3-3σ, the matching profile of hepatocellular carcinoma (HCC). Gene-circuit-loaded cells distinguished HCC from other cells and released therapeutic antibodies, exhibiting in vitro and in vivo therapeutic effects.

Designing cell function: assembly of synthetic gene circuits for cell biology applications

Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Synthetic control systems for high performance gene expression in mammalian cells

Tunable induction of gene expression is an essential tool in biology and biotechnology. In spite of that, current induction systems often exhibit unpredictable behavior and performance shortcomings, including high sensitivity to transactivator dosage and plasmid take-up variation, and excessive consumption of cellular resources. To mitigate these limitations, we introduce here a novel family of gene expression control systems of varying complexity with significantly enhanced performance. These include: (i) an incoherent feedforward circuit that exhibits output tunability and robustness to plasmid take-up variation; (ii) a negative feedback circuit that reduces burden and provides robustness to transactivator dosage variability; and (iii) a new hybrid circuit integrating negative feedback and incoherent feedforward that combines the benefits of both. As with endogenous circuits, the complexity of our genetic controllers is not gratuitous, but is the necessary outcome of more stringent performance requirements. We demonstrate the benefits of these controllers in two applications. In a culture of CHO cells for protein manufacturing, the circuits result in up to a 2.6-fold yield improvement over a standard system. In human-induced pluripotent stem cells they enable precisely regulated expression of an otherwise poorly tolerated gene of interest, resulting in a significant increase in the viability of the transfected cells.

Synthetic gene circuits for the detection, elimination and prevention of disease

In living organisms, naturally evolved sensors that constantly monitor and process environmental cues trigger corrective actions that enable the organisms to cope with changing conditions. Such natural processes have inspired biologists to construct synthetic living sensors and signalling pathways, by repurposing naturally occurring proteins and by designing molecular building blocks de novo, for customized diagnostics and therapeutics. In particular, designer cells that employ user-defined synthetic gene circuits to survey disease biomarkers and to autonomously re-adjust unbalanced pathological states can coordinate the production of therapeutics, with controlled timing and dosage. Furthermore, tailored genetic networks operating in bacterial or human cells have led to cancer remission in experimental animal models, owing to the network's unprecedented specificity. Other applications of designer cells in infectious, metabolic and autoimmune diseases are also being explored. In this Review, we describe the biomedical applications of synthetic gene circuits in major disease areas, and discuss how the first genetically engineered devices developed on the basis of synthetic-biology principles made the leap from the laboratory to the clinic.

Genetic programs can be compressed and autonomously decompressed in live cells

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes 1-13 and genetically encoded circuits in live cells 14-21 . Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity 22 of two bits per nucleotide 23 . In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum 24 . We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit 25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits 15,18,26-28 . In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

A Workflow for In Vivo Evaluation of Candidate Inputs and Outputs for Cell Classifier Gene Circuits

Cell classifier gene circuits that integrate multiple molecular inputs to restrict the expression of therapeutic outputs to cancer cells have the potential to result in efficacious and safe cancer therapies. Preclinical translation of the hitherto developments requires creating the conditions where the animal model, the delivery platform, in vivo expression levels of the inputs, and the efficacy of the output, all come together to enable detailed evaluation of the fully assembled circuits. Here we show an integrated workflow that addresses these issues and builds the framework for preclinical classifier studies using the design framework of microRNA (miRNA, miR)-based classifier gene circuits. Specifically, we employ HCT-116 colorectal cancer cell xenograft in an experimental mouse metastatic liver tumor model together with Adeno-associated virus (AAV) vector delivery platform. Novel engineered AAV-based constructs are used to validate in vivo the candidate inputs miR-122 and miR-7 and, separately, the cytotoxic output HSV-TK/ganciclovir. We show that while the data are largely consistent with expectations, crucial insights are gained that could not have been obtained in vitro. The results highlight the importance of detailed stepwise interrogation of the experimental parameters as a necessary step toward clinical translation of synthetic gene circuits.

Exploring the Potential Application of Short Non-Coding RNA-Based Genetic Circuits in Chinese Hamster Ovary Cells

The majority of cell engineering for recombinant protein production to date has relied on traditional genetic engineering strategies, such as gene overexpression and gene knock-outs, to substantially improve the production capabilities of Chinese Hamster Ovary (CHO) cells. However, further improvements in cellular productivity or control over product quality is likely to require more sophisticated rational approaches to coordinate and balance cellular pathways. For these strategies to be implemented, novel molecular tools need to be developed to facilitate more refined control of gene expression. Multiple gene control strategies are developed over the last decades in the field of synthetic biology, including DNA and RNA-based systems, which allows tight and timely control over gene expression. microRNAs has received a lot of attention over the last decade in the CHO field and are used to engineer and improve CHO cells. In this review we focus on microRNA-based gene control systems and discuss their potential use as tools rather than targets in order to gain better control over gene expression.

Synthetic Biology-The Synthesis of Biology

Synthetic biology concerns the engineering of man-made living biomachines from standardized components that can perform predefined functions in a (self-)controlled manner. Different research strategies and interdisciplinary efforts are pursued to implement engineering principles to biology. The "top-down" strategy exploits nature's incredible diversity of existing, natural parts to construct synthetic compositions of genetic, metabolic, or signaling networks with predictable and controllable properties. This mainly application-driven approach results in living factories that produce drugs, biofuels, biomaterials, and fine chemicals, and results in living pills that are based on engineered cells with the capacity to autonomously detect and treat disease states in vivo. In contrast, the "bottom-up" strategy seeks to be independent of existing living systems by designing biological systems from scratch and synthesizing artificial biological entities not found in nature. This more knowledge-driven approach investigates the reconstruction of minimal biological systems that are capable of performing basic biological phenomena, such as self-organization, self-replication, and self-sustainability. Moreover, the syntheses of artificial biological units, such as synthetic nucleotides or amino acids, and their implementation into polymers inside living cells currently set the boundaries between natural and artificial biological systems. In particular, the in vitro design, synthesis, and transfer of complete genomes into host cells point to the future of synthetic biology: the creation of designer cells with tailored desirable properties for biomedicine and biotechnology.

Model-guided combinatorial optimization of complex synthetic gene networks

Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time. Here we describe a way to immediately expand the range of biologically detectable molecules by systematically designing metabolic pathways that transform nondetectable molecules into molecules for which sensors already exist. We leveraged computer-aided design to predict such sensing-enabling metabolic pathways, and we built several new whole-cell biosensors for molecules such as cocaine, parathion, hippuric acid, and nitroglycerin.

Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells

One of the goals of synthetic biology is to develop programmable artificial gene networks that can transduce multiple endogenous molecular cues to precisely control cell behavior. Realizing this vision requires interfacing natural molecular inputs with synthetic components that generate functional molecular outputs. Interfacing synthetic circuits with endogenous mammalian transcription factors has been particularly difficult. Here, we describe a systematic approach that enables integration and transduction of multiple mammalian transcription factor inputs by a synthetic network. The approach is facilitated by a proportional amplifier sensor based on synergistic positive autoregulation. The circuits efficiently transduce endogenous transcription factor levels into RNAi, transcriptional transactivation, and site-specific recombination. They also enable AND logic between pairs of arbitrary transcription factors. The results establish a framework for developing synthetic gene networks that interface with cellular processes through transcriptional regulators.

Synthetic recombinase-based state machines in living cells

State machines underlie the sophisticated functionality behind human-made and natural computing systems that perform order-dependent information processing. We developed a recombinase-based framework for building state machines in living cells by leveraging chemically controlled DNA excision and inversion operations to encode states in DNA sequences. This strategy enables convenient readout of states (by sequencing and/or polymerase chain reaction) as well as complex regulation of gene expression. We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states. These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines. Our scalable framework should enable new strategies for recording and studying how combinational and temporal events regulate complex cell functions and for programming sophisticated cell behaviors.

Engineering Gene Circuits for Mammalian Cell-Based Applications

Synthetic gene switches are basic building blocks for the construction of complex gene circuits that transform mammalian cells into useful cell-based machines for next-generation biotechnological and biomedical applications. Ligand-responsive gene switches are cellular sensors that are able to process specific signals to generate gene product responses. Their involvement in complex gene circuits results in sophisticated circuit topologies that are reminiscent of electronics and that are capable of providing engineered cells with the ability to memorize events, oscillate protein production, and perform complex information-processing tasks. Microencapsulated mammalian cells that are engineered with closed-loop gene networks can be implanted into mice to sense disease-related input signals and to process this information to produce a custom, fine-tuned therapeutic response that rebalances animal metabolism. Progress in gene circuit design, in combination with recent breakthroughs in genome engineering, may result in tailored engineered mammalian cells with great potential for future cell-based therapies.

Precision multidimensional assay for high-throughput microRNA drug discovery

Development of drug discovery assays that combine high content with throughput is challenging. Information-processing gene networks can address this challenge by integrating multiple potential targets of drug candidates' activities into a small number of informative readouts, reporting simultaneously on specific and non-specific effects. Here we show a family of networks implementing this concept in a cell-based drug discovery assay for miRNA drug targets. The networks comprise multiple modules reporting on specific effects towards an intended miRNA target, together with non-specific effects on gene expression, off-target miRNAs and RNA interference pathway. We validate the assays using known perturbations of on- and off-target miRNAs, and evaluate an ∼700 compound library in an automated screen with a follow-up on specific and non-specific hits. We further customize and validate assays for additional drug targets and non-specific inputs. Our study offers a novel framework for precision drug discovery assays applicable to diverse target families.

Progress in drug discovery can be hampered by a limited exploration of chemical space and the difficulty in assessing the full range of drug candidates' effects on living cells. Here the authors describe a cell-based assay to distinguish between off-target and specific effects of candidate compounds targeting micro RNAs.

Rationally Designed MicroRNA-Based Genetic Classifiers Target Specific Neurons in the Brain

Targeting transgene expression to specific cell types in vivo has proven instrumental in characterizing the functional role of defined cell populations. Genetic classifiers, synthetic transgene constructs designed to restrict expression to particular classes of cells, commonly rely on transcriptional promoters to define cellular specificity. However, the large size of many natural promoters complicates their use in viral vectors, an important mode of transgene delivery in the brain and in human gene therapy. Here, we expanded upon an emerging classifier platform, orthogonal to promoter-based strategies, that exploits endogenous microRNA regulation to target gene expression. Such classifiers have been extensively explored in other tissues; however, their use in the nervous system has thus far been limited to targeting gene expression between neurons and supporting cells. Here, we tested the possibility of using combinatory microRNA regulation to specify gene targeting between neuronal subtypes, and successfully targeted inhibitory cells in the neocortex. These classifiers demonstrate the feasibility of designing a new generation of microRNA-based neuron-type- and brain-region-specific gene expression targeting neurotechnologies.

Mammalian designer cells: Engineering principles and biomedical applications

Biotechnology is a widely interdisciplinary field focusing on the use of living cells or organisms to solve established problems in medicine, food production and agriculture. Synthetic biology, the science of engineering complex biological systems that do not exist in nature, continues to provide the biotechnology industry with tools, technologies and intellectual property leading to improved cellular performance. One key aspect of synthetic biology is the engineering of deliberately reprogrammed designer cells whose behavior can be controlled over time and space. This review discusses the most commonly used techniques to engineer mammalian designer cells; while control elements acting on the transcriptional and translational levels of target gene expression determine the kinetic and dynamic profiles, coupling them to a variety of extracellular stimuli permits their remote control with user-defined trigger signals. Designer mammalian cells with novel or improved biological functions not only directly improve the production efficiency during biopharmaceutical manufacturing but also open the door for cell-based treatment strategies in molecular and translational medicine. In the future, the rational combination of multiple sets of designer cells could permit the construction and regulation of higher-order systems with increased complexity, thereby enabling the molecular reprogramming of tissues, organisms or even populations with highest precision.

Digital switching in a biosensor circuit via programmable timing of gene availability

Transient delivery of gene circuits is required in many potential applications of synthetic biology, yet pre-steady-state processes that dominate this delivery route pose significant challenges for robust circuit deployment. Here we show that site-specific recombinases can rectify undesired effects by programmable timing of gene availability in multi-gene circuits. We exemplify the concept with a proportional sensor for endogenous microRNA and show dramatic reduction in its ground state leakage thanks to desynchronization of circuit’s repressor components and their repression target. The new sensors display dynamic range of up to 1000-fold compared to 20-fold in the standard configuration. We applied the approach to classify cell types based on miRNA expression profile and measured > 200-fold output differential between positively- and negatively-identified cells. We also showed major improvement of specificity with cytotoxic output. Our study opens new venues in gene circuit design via judicious temporal control of circuits’ genetic makeup.