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.

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.

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.

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.

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.

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.

Multi-input RNAi-based logic circuit for identification of specific cancer cells.

Engineered biological systems that integrate multi-input sensing, sophisticated information processing, and precisely regulated actuation in living cells could be useful in a variety of applications. For example, anticancer therapies could be engineered to detect and respond to complex cellular conditions in individual cells with high specificity. Here, we show a scalable transcriptional/posttranscriptional synthetic regulatory circuit--a cell-type "classifier"--that senses expression levels of a customizable set of endogenous microRNAs and triggers a cellular response only if the expression levels match a predetermined profile of interest. We demonstrate that a HeLa cancer cell classifier selectively identifies HeLa cells and triggers apoptosis without affecting non-HeLa cell types. This approach also provides a general platform for programmed responses to other complex cell states.

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.

DNA molecule provides a computing machine with both data and fuel.

The unique properties of DNA make it a fundamental building block in the fields of supramolecular chemistry, nanotechnology, nano-circuits, molecular switches, molecular devices, and molecular computing. In our recently introduced autonomous molecular automaton, DNA molecules serve as input, output, and software, and the hardware consists of DNA restriction and ligation enzymes using ATP as fuel. In addition to information, DNA stores energy, available on hybridization of complementary strands or hydrolysis of its phosphodiester backbone. Here we show that a single DNA molecule can provide both the input data and all of the necessary fuel for a molecular automaton. Each computational step of the automaton consists of a reversible software molecule input molecule hybridization followed by an irreversible software-directed cleavage of the input molecule, which drives the computation forward by increasing entropy and releasing heat. The cleavage uses a hitherto unknown capability of the restriction enzyme FokI, which serves as the hardware, to operate on a noncovalent software input hybrid. In the previous automaton, software input ligation consumed one software molecule and two ATP molecules per step. As ligation is not performed in this automaton, a fixed amount of software and hardware molecules can, in principle, process any input molecule of any length without external energy supply. Our experiments demonstrate 3 x 10(12) automata per microl performing 6.6 x 10(10) transitions per second per microl with transition fidelity of 99.9%, dissipating about 5 x 10(-9) W microl as heat at ambient temperature.