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.

A self-assembling peptidic platform to boost the cellular uptake and nuclear delivery of oligonucleotides.

The design of non-viral vectors that efficiently deliver genetic materials into cells, in particular to the nucleus, remains a major challenge in gene therapy and vaccine development. To tackle the problems associated with cellular uptake and nuclear targeting, here we introduce a delivery platform based on the self-assembly of an amphiphilic peptide carrying an N-terminal KRKR sequence that functions as a nuclear localization signal (NLS). By means of a single-step self-assembly process, the amphiphilic peptides afford the generation of NLS-functionalized multicompartment micellar nanostructures that can embed various oligonucleotides between their individual compartments. Detailed physicochemical, cellular and ultrastructural analyses demonstrated that integrating an NLS in the hydrophilic domain of the peptide along with tuning its hydrophobic domain led to self-assembled DNA-loaded multicompartment micelles (MCMs) with enhanced cellular uptake and nuclear translocation. We showed that the nuclear targeting ensued via the NLS interaction with the nuclear transport receptors of the karyopherin family. Importantly, we observed that the treatment of MCF-7 cells with NLS-MCMs loaded with anti-BCL2 antisense oligonucleotides resulted in up to 86% knockdown of BCL2, an inhibitor of apoptosis that is overexpressed in more than half of all human cancers. We envision that this platform can be used to efficiently entrap and deliver diverse genetic payloads to the nucleus and find applications in basic research and biomedicine.

Rational design and construction of multi-copy biomanufacturing islands in mammalian cells.

Cell line development is a critical step in the establishment of a biopharmaceutical manufacturing process. Current protocols rely on random transgene integration and amplification. Due to considerable variability in transgene integration profiles, this workflow results in laborious screening campaigns before stable producers can be identified. Alternative approaches for transgene dosage increase and integration are therefore highly desirable. In this study, we present a novel strategy for the rapid design, construction, and genomic integration of engineered multiple-copy gene constructs consisting of up to 10 gene expression cassettes. Key to this strategy is the diversification, at the sequence level, of the individual gene cassettes without altering their protein products. We show a computational workflow for coding and regulatory sequence diversification and optimization followed by experimental assembly of up to nine gene copies and a sentinel reporter on a contiguous scaffold. Transient transfections in CHO cells indicates that protein expression increases with the gene copy number on the scaffold. Further, we stably integrate these cassettes into a pre-validated genomic locus. Altogether, our findings point to the feasibility of engineering a fully mapped multi-copy recombinant protein 'production island' in a mammalian cell line with greatly reduced screening effort, improved stability, and predictable product titers.

An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice.

Clinical translation of multi-input biomolecular computing systems holds potential to lead to disease-tailored, data-driven rational design of next-generation therapeutic modalities. However, practical demonstrations of this potential are lacking. Here, we developed a clinically translatable approach for the design and implementation of therapeutic agents comprising biomolecular multi-input logic modules for precision cell targeting, compatible with adeno-associated virus (AAV) vectors. We used this approach to engineer an AAV-encoded gene therapy prototype that, when delivered systemically, successfully treated hepatocellular carcinoma in an orthotopic mouse tumor model. The therapy performed a molecular-scale computation over multiple transcriptional and microRNA inputs based on the differential molecular profiles of tumor and nontumor cells, to guide the activation of a herpes simplex virus thymidine kinase (HSV-TK) effector gene. Multi-input computation in individual cells was necessary and sufficient to drive in vivo and in situ tumor-specific expression of HSV-TK with minimal concomitant expression in nontumor liver and other organs. Intravenous vector injection in combination with ganciclovir resulted in marked reduction in tumor burden in treated mice compared with controls, without negative effects on general well-being or weight. The therapeutic approach has the capacity to perform logical integration of diseased and healthy cell­specific molecular inputs to precisely regulate therapeutic effector gene expression and is a promising avenue for the next generation of cancer therapies. Moreover, our systematic data-driven workflow illustrates how gene expression data can shape the molecular composition of future therapeutic candidates.

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.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.

A self-assembling amphiphilic peptide nanoparticle for the efficient entrapment of DNA cargoes up to 100 nucleotides in length.

To overcome the low efficiency and cytotoxicity associated with most non-viral DNA delivery systems we developed a purely peptidic self-assembling system that is able to entrap single- and double-stranded DNA of up to 100 nucleotides in length. (HR)3gT peptide design consists of a hydrophilic domain prone to undergo electrostatic interactions with DNA cargo, and a hydrophobic domain at a ratio that promotes the self-assembly into multi-compartment micellar nanoparticles (MCM-NPs). Self-assembled (HR)3gT MCM-NPs range between 100 to 180 nm which is conducive to a rapid and efficient uptake by cells. (HR)3gT MCM-NPs had no adverse effects on HeLa cell viability. In addition, they exhibit long-term structural stability at 4 °C but at 37 °C, the multi-micellar organization disassembles overtime which demonstrates their thermo-responsiveness. The comparison of (HR)3gT to a shorter, less charged H3gT peptide indicates that the additional arginine residues result in the incorporation of longer DNA segments, an improved DNA entrapment efficiency and an increase cellular uptake. Our unique non-viral system for DNA delivery sets the stage for developing amphiphilic peptide nanoparticles as candidates for future systemic gene delivery.

Artificial signaling in mammalian cells enabled by prokaryotic two-component system.

Augmenting live cells with new signal transduction capabilities is a key objective in genetic engineering and synthetic biology. We showed earlier that two-component signaling pathways could function in mammalian cells, albeit while losing their ligand sensitivity. Here, we show how to transduce small-molecule ligands in a dose-dependent fashion into gene expression in mammalian cells using two-component signaling machinery. First, we engineer mutually complementing truncated mutants of a histidine kinase unable to dimerize and phosphorylate the response regulator. Next, we fuse these mutants to protein domains capable of ligand-induced dimerization, which restores the phosphoryl transfer in a ligand-dependent manner. Cytoplasmic ligands are transduced by facilitating mutant dimerization in the cytoplasm, while extracellular ligands trigger dimerization at the inner side of a plasma membrane. These findings point to the potential of two-component regulatory systems as enabling tools for orthogonal signaling pathways in mammalian cells.

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.

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.

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.

Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.

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.

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.

Complexity from Simple Building Blocks: Engineering Large-scale Information-processing Networks from Molecules.

Complexity in molecular systems can manifest itself either structurally or functionally. One of the more complex functions encountered in the natural world is that of information processing, or computation. Similarly, artificial cells will require this capacity to fully exploit their potential. Here I review the state of the art in the field, describe our contribution to this challenge in the framework of NCCR Molecular Systems Engineering, and propose an outlook for future efforts.

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.

Highly modular bow-tie gene circuits with programmable dynamic behaviour.

Synthetic gene circuits often require extensive mutual optimization of their components for successful operation, while modular and programmable design platforms are rare. A possible solution lies in the 'bow-tie' architecture, which stipulates a focal component-a 'knot'-uncoupling circuits' inputs and outputs, simplifying component swapping, and introducing additional layer of control. Here we construct, in cultured human cells, synthetic bow-tie circuits that transduce microRNA inputs into protein outputs with independently programmable logical and dynamic behaviour. The latter is adjusted via two different knot configurations: a transcriptional activator causing the outputs to track input changes reversibly, and a recombinase-based cascade, converting transient inputs into permanent actuation. We characterize the circuits in HEK293 cells, confirming their modularity and scalability, and validate them using endogenous microRNA inputs in additional cell lines. This platform can be used for biotechnological and biomedical applications in vitro, in vivo and potentially in human therapy.