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.

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.

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.

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.

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.

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.

Biomolecular computing systems: principles, progress and potential.

The task of information processing, or computation, can be performed by natural and man-made 'devices'. Man-made computers are made from silicon chips, whereas natural 'computers', such as the brain, use cells and molecules. Computation also occurs on a much smaller scale in regulatory and signalling pathways in individual cells and even within single biomolecules. Indeed, much of what we recognize as life results from the remarkable capacity of biological building blocks to compute in highly sophisticated ways. Rational design and engineering of biological computing systems can greatly enhance our ability to study and to control biological systems. Potential applications include tissue engineering and regeneration and medical treatments. This Review introduces key concepts and discusses recent progress that has been made in biomolecular computing.

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.

Transplantation of prokaryotic two-component signaling pathways into mammalian cells.

Signaling pathway engineering is a promising route toward synthetic biological circuits. Histidine-aspartate phosphorelays are thought to have evolved in prokaryotes where they form the basis for two-component signaling. Tyrosine-serine-threonine phosphorelays, exemplified by MAP kinase cascades, are predominant in eukaryotes. Recently, a prokaryotic two-component pathway was implemented in a plant species to sense environmental trinitrotoluene. We reasoned that "transplantation" of two-component pathways into mammalian host could provide an orthogonal and diverse toolkit for a variety of signal processing tasks. Here we report that two-component pathways could be partially reconstituted in mammalian cell culture and used for programmable control of gene expression. To enable this reconstitution, coding sequences of histidine kinase (HK) and response regulator (RR) components were codon-optimized for human cells, whereas the RRs were fused with a transactivation domain. Responsive promoters were furnished by fusing DNA binding sites in front of a minimal promoter. We found that coexpression of HKs and their cognate RRs in cultured mammalian cells is necessary and sufficient to strongly induce gene expression even in the absence of pathways' chemical triggers in the medium. Both loss-of-function and constitutive mutants behaved as expected. We further used the two-component signaling pathways to implement two-input logical AND, NOR, and OR gene regulation. Thus, two-component systems can be applied in different capacities in mammalian cells and their components can be used for large-scale synthetic gene circuits.

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 the pre-steady-state processes that dominate this delivery route pose major challenges for robust circuit deployment. Here we show that site-specific recombinases can rectify undesired effects by programmable timing of gene availability in multigene circuits. We exemplify the concept with a proportional sensor for endogenous microRNA (miRNA) and show a marked reduction in its ground state leakage due to desynchronization of the circuit's repressor components and their repression target. The new sensors display a dynamic range of up to 1,000-fold compared to 20-fold in the standard configuration. We applied the approach to classify cell types on the basis of miRNA expression profile and measured >200-fold output differential between positively and negatively identified cells. We also showed major improvements in specificity with cytotoxic output. Our study opens new venues in gene circuit design via judicious temporal control of circuits' genetic makeup.

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.

Rapid hierarchical assembly of medium-size DNA cassettes.

Synthetic biology applications call for efficient methods to generate large gene cassettes that encode complex gene circuits in order to avoid simultaneous delivery of multiple plasmids encoding individual genes. Multiple methods have been proposed to achieve this goal. Here, we describe a novel protocol that allows one-step cloning of up to four gene-size DNA fragments, followed by a second assembly of these concatenated sequences into large circular DNA. The protocols described here comprise a simple, cheap and fast solution for routine construction of cassettes with up to 10 gene-size components.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template.

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.

Logic integration of mRNA signals by an RNAi-based molecular computer.

Synthetic in vivo molecular 'computers' could rewire biological processes by establishing programmable, non-native pathways between molecular signals and biological responses. Multiple molecular computer prototypes have been shown to work in simple buffered solutions. Many of those prototypes were made of DNA strands and performed computations using cycles of annealing-digestion or strand displacement. We have previously introduced RNA interference (RNAi)-based computing as a way of implementing complex molecular logic in vivo. Because it also relies on nucleic acids for its operation, RNAi computing could benefit from the tools developed for DNA systems. However, these tools must be harnessed to produce bioactive components and be adapted for harsh operating environments that reflect in vivo conditions. In a step toward this goal, we report the construction and implementation of biosensors that 'transduce' mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments. We further integrate the sensors with our RNAi 'computational' module to evaluate two-input logic functions on mRNA concentrations. Our results show how RNA strand exchange can expand the utility of RNAi computing and point toward the possibility of using strand exchange in a native biological setting.

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.

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.

An autonomous molecular computer for logical control of gene expression.

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

A universal RNAi-based logic evaluator that operates in mammalian cells.

Molecular automata that combine sensing, computation and actuation enable programmable manipulation of biological systems. We use RNA interference (RNAi) in human kidney cells to construct a molecular computing core that implements general Boolean logic to make decisions based on endogenous molecular inputs. The state of an endogenous input is encoded by the presence or absence of 'mediator' small interfering RNAs (siRNAs). The encoding rules, combined with a specific arrangement of the siRNA targets in a synthetic gene network, allow direct evaluation of any Boolean expression in standard forms using siRNAs and indirect evaluation using endogenous inputs. We demonstrate direct evaluation of expressions with up to five logic variables. Implementation of the encoding rules through sensory up- and down-regulatory links between the inputs and siRNA mediators will allow arbitrary Boolean decision-making using these inputs.

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.