Search

Receptors enable cells to detect, process and respond to information about their environments. Over the past two decades, synthetic biologists have repurposed physical parts and concepts from natural receptors to engineer synthetic receptors. These technologies implement customized sense-and-respond programs that link a cell's interaction with extracellular and intracellular cues to user-defined responses. When combined with tools for information processing, these advances enable programming of sophisticated customized functions. In recent years, the library of synthetic receptors and their capabilities has substantially evolved-a term we employ here to mean systematic improvement and expansion. Here, we survey the existing mammalian synthetic biology toolkit of protein-based receptors and signal-processing components, highlighting efforts to evolve and integrate some of the foundational synthetic receptor systems. We then propose a generalized strategy for engineering and improving receptor systems to meet defined functional objectives called a 'metric-enabled approach for synthetic receptor engineering' (MEASRE).

Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features—CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression—individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.

The precise mechanism of transcription termination of the eukaryotic RNA polymerase III (Pol III) has been a subject of considerable debate. Although previous studies have clearly shown that multiple uracils at the end of RNA transcripts are required for Pol III termination, the effects of upstream RNA secondary structure in the nascent transcript on transcriptional termination is still unclear. To address this, we developed an in cellulo Pol III transcription termination assay using the recently developed Tornado-Corn RNA aptamer system to create a Pol III-transcribed RNA that produces a detectable fluorescent signal when transcribed in human cells. To study the effects of RNA sequence and structure on Pol III termination, we systematically varied the sequence context upstream of the aptamer and identified sequence characteristics that enhance or diminish termination. For transcription from Pol III type 3 promoters, we found that only poly-U tracts longer than the average length found in the human genome efficiently terminate Pol III transcription without RNA secondary structure elements. We observed that RNA secondary structure elements placed in proximity to shorter poly-U tracts induced termination, and RNA secondary structure by itself was not sufficient to induce termination. For Pol III type 2 promoters, we found that the shorter poly-U tract lengths of 4 uracils were sufficient to induce termination. These findings demonstrate a key role for sequence and structural elements within Pol III-transcribed nascent RNA for efficient transcription termination, and demonstrate a generalizable assay for characterizing Pol III transcription in human cells.

Naturally evolved metabolite-responsive biosensors enable applications in metabolic engineering, ranging from screening large genetic libraries to dynamically regulating biosynthetic pathways. However, there are many metabolites for which a natural biosensor does not exist. To address this need, we developed a general method for converting metabolite-binding proteins into metabolite-responsive transcription factors—Biosensor Engineering by Random Domain Insertion (BERDI). This approach takes advantage of an in vitro transposon insertion reaction to generate all possible insertions of a DNA-binding domain into a metabolite-binding protein, followed by fluorescence activated cell sorting to isolate functional biosensors. To develop and evaluate the BERDI method, we generated a library of candidate biosensors in which a zinc finger DNA-binding domain was inserted into maltose binding protein, which served as a model well-studied metabolite-binding protein. Library diversity was characterized by several methods, a selection scheme was deployed, and ultimately several distinct and functional maltose-responsive transcriptional biosensors were identified. We hypothesize that the BERDI method comprises a generalizable strategy that may ultimately be applied to convert a wide range of metabolite-binding proteins into novel biosensors for applications in metabolic engineering and synthetic biology.

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. This new technology will streamline the generation and use of split protein systems for diverse applications.

Macrophages are ubiquitous innate immune cells that play a central role in health and disease by adopting distinct phenotypes, which are broadly divided into classical inflammatory responses and alternative responses that promote immune suppression and wound healing. Although macrophages are attractive therapeutic targets, incomplete understanding of this functional choice limits clinical manipulation. While individual stimuli, pathways, and genes involved in macrophage functional responses have been identified, how macrophages evaluate complex in vivo milieus comprising multiple divergent stimuli remains poorly understood. Here, we used combinations of “incoherent” stimuli—those that individually promote distinct macrophage phenotypes—to elucidate how the immunosuppressive, IL-10-driven macrophage phenotype is induced, maintained, and modulated under such combinatorial stimuli. The IL-10-induced immunosuppressive phenotype was largely insensitive to co-administered IL-12, which has been reported to modulate macrophage phenotype, but maintaining the immunosuppressive phenotype required sustained exposure to IL-10. Our data implicate the intracellular protein, BCL3, as a key mediator of the IL-10-driven phenotype. Notably, co-administration of IFN-γ disrupted an IL-10-mediated positive feedback loop that may reinforce the immunosuppressive phenotype. This novel combinatorial perturbation approach thus generated new insights into macrophage decision making and local immune network function.

Biomaterial scaffolds are central to many tissue engineering strategies as they create a space for tissue growth and provide a support for cell adhesion and migration. However, biomaterial implantation results in unavoidable injury resulting in an inflammatory response, which can impair integration with the host and tissue regeneration. Toward the goal of reducing inflammation, we investigated the hypothesis that a lentiviral gene therapy-based approach to localized and sustained IL-10 expression at a scaffold could modulate the number, relative proportions, and cytokine production of infiltrating leukocyte populations. Flow cytometry was used to quantify infiltration of six leukocyte populations for 21 days following implantation of PLG scaffolds into intraperitoneal fat. Leukocytes with innate immune functions (i.e., macrophages, dendritic cells, neutrophils) were most prevalent at early time points, while T lymphocytes became prevalent by day 14. Reporter gene delivery indicated that transgene expression persisted at the scaffold for up to 28 days and macrophages were the most common leukocyte transduced, while transduced dendritic cells expressed the greatest levels of transgene. IL-10 delivery decreased leukocyte infiltration by 50% relative to controls, increased macrophage IL-10 expression, and decreased macrophage, dendritic cell, and CD4 T cell IFN-γ expression. Thus, IL-10 gene delivery significantly decreased inflammation following scaffold implant into the intraperitoneal fat, in part by modulating cytokine expression of infiltrating leukocytes.

Technologies for engineering immune cells to recognize features on cancer cells are transforming oncology. Synthetic biologists now expand this approach by conferring therapeutic functions to nonimmune cells and by programming cells to sense and respond to a new class of physiological cues.

Maturation of dendritic cells (DC) to competent APC is essential for the generation of acquired immunity and is a major function of adjuvants. dsRNA, a molecular signature of viral infection, drives DC maturation by activating TLR3, but the size of dsRNA required to activate DC and the expression patterns of TLR3 protein in DC subsets have not been established. In this article, we show that cross-priming CD8α+ and CD103+ DC subsets express much greater levels of TLR3 than other DC. In resting DC, TLR3 is located in early endosomes and other intracellular compartments but migrates to LAMP1+ endosomes on stimulation with a TLR3 ligand. Using homogeneous dsRNA oligonucleotides (ONs) ranging in length from 25 to 540 bp, we observed that a minimum length of ∼90 bp was sufficient to induce CD86, IL-12p40, IFN-β, TNF-α, and IL-6 expression, and to mature DC into APC that cross-presented exogenous Ags to CD8+ T cells. TLR3 was essential for activation of DC by dsRNA ONs, and the potency of activation increased with dsRNA length and varied between DC subsets. In vivo, dsRNA ONs, in a size-dependent manner, served as adjuvants for the generation of Ag-specific CTL and for inducing protection against lethal challenge with influenza virus when given with influenza nucleoprotein as an immunogen. These results provide the basis for the development of TLR3-specific adjuvants capable of inducing immune responses tailored for viral pathogens.

Short interfering RNAs (siRNA) are routinely used in the laboratory to induce targeted gene silencing by RNA interference, and increasingly, this technology is being translated to the clinic. However, there are multiple mechanisms by which siRNA may be recognized by receptors of the innate immune system, including both endosomal Toll-like receptors and cytoplasmic receptors. Signaling through these receptors may induce multiple nonspecific effects, including general reductions in gene expression and the production of type I interferons and inflammatory cytokines, which can lead to systemic inflammation in vivo. The pattern of immune activation varies depending upon the types of cells and receptors that are stimulated by a particular siRNA. Although we are still discovering the mechanisms by which these recognition events occur, our current understanding provides useful guidelines for avoiding immune activation. In this minireview, we present a design-based approach for developing siRNA-based experiments and therapies that evade innate immune recognition and control nonspecific effects. We describe strategies and trade-offs related to siRNA design considerations including the choice of siRNA target sequence, chemical modifications to the RNA backbone and the influence of the delivery method on immune activation. Finally, we provide suggestions for conducting appropriate controls for siRNA experiments, because some commonly employed strategies do not adequately account for known nonspecific effects and can lead to misinterpretation of the data. By incorporating these principles into siRNA design, it is generally possible to control nonspecific effects, and doing so will help to best utilize this powerful technology for both basic science and therapeutics.

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation., Author Summary Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.

Exosomes are secreted extracellular vesicles that mediate intercellular transfer of cellular contents and are attractive vehicles for therapeutic delivery of bimolecular cargo such as nucleic acids, proteins, and even drugs. Efficient exosome-mediated delivery in vivo requires targeting vesicles for uptake by specific recipient cells. Although exosomes have been successfully targeted to several cellular receptors by displaying peptides on the surface of the exosomes, identifying effective exosome-targeting peptides for other receptors has proven challenging. Furthermore, the biophysical rules governing targeting peptide success remain poorly understood. To evaluate one factor potentially limiting exosome delivery, we investigated whether peptides displayed on the exosome surface are degraded during exosome biogenesis, for example by endosomal proteases. Indeed, peptides fused to the N terminus of exosome-associated transmembrane protein Lamp2b were cleaved in samples derived from both cells and exosomes. To suppress peptide loss, we engineered targeting peptide-Lamp2b fusion proteins to include a glycosylation motif at various positions. Introduction of this glycosylation motif both protected the peptide from degradation and led to an increase in overall Lamp2b fusion protein expression in both cells and exosomes. Moreover, glycosylation-stabilized peptides enhanced targeted delivery of exosomes to neuroblastoma cells, demonstrating that such glycosylation does not ablate peptide-target interactions. Thus, we have identified a strategy for achieving robust display of targeting peptides on the surface of exosomes, which should facilitate the evaluation and development of new exosome-based therapeutics.

The field of directed RNA interference (RNAi) has rapidly developed into a highly promising approach for specifically down regulating genes to alleviate disease pathology. This technology is especially well-suited to treating viral infections, and numerous examples now illustrate that a wide range of viruses can be inhibited with RNAi, both in vitro and in vivo. One principle that has arisen from this work is that antiviral RNAi therapies must be tailored to the unique life cycle of each pathogen, including the choice of delivery vehicle, route of administration, gene(s) targeted and regulation and duration of RNAi induction. Although effective strategies will be customized to each virus, all such therapies must overcome similar challenges. Importantly, treatment strategies must compensate for the inevitable fact that viral genome sequences evolve extremely rapidly, and computational and bioinformatics approaches may aid in the development of therapies that resist viral escape. Furthermore, all RNAi strategies involve the delivery of nucleic acids to target cells, and all will therefore benefit from the development of enhanced gene design and delivery technologies. Here, we review the substantial progress that has been made towards identifying effective antiviral RNAi targets and discuss strategies for translating these findings into effective clinical therapies.

Toll-like receptors (TLRs) mediate immune recognition of both microbial infections and tissue damage. Aberrant TLR signaling promotes disease; thus, understanding the regulation of TLR signaling is of medical relevance. Although downstream mediators of TLR signaling have been identified, the detailed mechanism by which ligand binding-mediated dimerization induces downstream signaling remains poorly understood. Here, we investigate this question for TLR4, which mediates responsiveness to bacterial LPS and drives inflammatory disease. TLR4 exhibits structural and functional features that are unique among TLRs, including responsiveness to a wide variety of ligands. However, the connection between these structural features and the regulation of signaling is not clear. Here, we investigated how the unique intracellular structures of TLR4 contribute to receptor signaling. Key conclusions include the following. 1) The unique intracellular linker of TLR4 is important for achieving LPS-inducible signaling via Toll/IL-1 receptor (TIR) domain-containing adapter-inducing interferon-β (TRIF) but less so for signaling via myeloid differentiation primary response 88 (MyD88). 2) Membrane-bound TLR4 TIR domains were sufficient to induce signaling. However, introducing long, flexible intracellular linkers neither induced constitutive signaling nor ablated LPS-inducible signaling. Thus, the initiation of TLR4 signaling is regulated by a mechanism that does not require tight geometric constraints. Together, these observations necessitate refining the model of TLR4 signal initiation. We hypothesize that TLR4 may interact with an inhibitory partner in the absence of ligand, via both TIR and extracellular domains of TLR4. In this speculative model, ligand binding induces dissociation of the inhibitory partner, triggering spontaneous, switchlike TIR domain homodimerization to initiate downstream signaling.

Extracellular vesicles (EVs) are secreted biological nanoparticles that have great potential as therapeutic delivery vehicles - they are well-tolerated in vivo and naturally capable of transferring RNA and proteins between cells. Our ability to engineer EVs as therapeutic delivery vehicles is limited by an incomplete understanding of how EVs load biomolecular cargo and deliver it to recipient cells. In particular, the biophysical rules governing mRNA and protein delivery by EVs have not been elucidated. Open questions include: Does size limit mRNA loading efficiency into EVs? To what extent is EV mRNA cargo translated in recipient cells? What factors impact the degree to which RNA and protein cargo are delivered to the cytoplasm of recipient cells?To quantitatively investigate the above questions, we leveraged our Targeted and Modular EV Loading (TAMEL) platform, which enables active loading of specific cargo RNA into EVs. TAMEL can enrich cargo mRNA loading into EVs up to 40-fold relative to passive loading. By directly comparing active loading efficiencies between mRNAs of different lengths, we characterized what type of RNAs can be loaded into EVs. While active loading of mRNA-length (> 1.5 kb) cargo molecules was significant, active loading was much more efficient for smaller (~0.5 kb) RNA molecules, providing the first direct evidence for the impact of cargo RNA size on loading into EVs. We next leveraged the TAMEL platform to elucidate the limiting steps in EV-mediated delivery of mRNA and protein to prostate cancer cells, as a therapeutically relevant model system. In this model system, we did not observe translation of EV-delivered mRNA in recipient cells, indicating this is a limiting step in functional delivery of EV cargo. In contrast, we observed robust EV-mediated delivery of dTomato reporter protein, and thus further explored EVs as therapeutic protein delivery vehicles.To probe the efficacy of EV-mediated therapeutic protein delivery, we investigated using EVs to deliver the prodrug converting enzyme cytosine deaminase fused to uracil phosphoribosyl transferase (CD-UPRT), which converts the prodrug 5-FC to the toxic 5-FU. Importantly, CD-UPRT can function without without requiring endosomal escape because both 5-FC and 5-FU are membrane permeable. We also explored strategies for EV-mediated delivery of Cas9 nuclease, which must overcome the EV loading barrier imposed by its NLS sequence as well as escape the endosome in recipient cells. To address the loading challenge, we investigated a strategy for conditional NLS reconstitution to allow enhanced loading of Cas9 into EVs. We then used Cas9 delivery to assess the degree to which EV-delivered proteins can escape the endosomal/lysosomal pathways and traffic to other subcellular locations. Altogether, our investigations elucidated key design rules and central limiting steps that may guide the further development and utilization of EVs as therapeutic biomolecule delivery vehicles.

Synthetic biology is a nascent technical discipline that seeks to enable the design and construction of novel biological systems to meet pressing societal needs. However, engineering biology still requires much trial and error because we lack effective approaches for connecting basic “parts” into higher-order networks that behave as predicted. Developing strategies for improving the performance and sophistication of our designs is informed by two overarching perspectives: “bottom-up” and “top-down” considerations. Using this framework, we describe a conceptual model for developing novel biological systems that function and interact with existing biological components in a predictable fashion. We discuss this model in the context of three topical areas: biochemical transformations, cellular devices and therapeutics, and approaches that expand the chemistry of life. Ten years after the construction of synthetic biology's first devices, the drive to look beyond what does exist to what can exist is ushering in an era of biology by design.

Toll-like receptor 3 (TLR3) recognizes double-stranded RNA (dsRNA), a molecular signature of most viruses, and triggers inflammatory responses that prevent viral spread. TLR3 ectodomains (ECDs) dimerize on oligonucleotides of at least 40 to 50 base pairs in length, the minimal length required for signal transduction. To establish the molecular basis for ligand binding and signaling, we determined the crystal structure of a complex between two mouse TLR3-ECDs and dsRNA at 3.4 angstrom resolution. Each TLR3-ECD binds dsRNA at two sites located at opposite ends of the TLR3 horseshoe, and an intermolecular contact between the two TLR3-ECD C-terminal domains coordinates and stabilizes the dimer. This juxtaposition could mediate downstream signaling by dimerizing the cytoplasmic Toll interleukin-1 receptor (TIR) domains. The overall shape of the TLR3-ECD does not change upon binding to dsRNA.

Recombinant adeno-associated virus (AAV) is a highly promising gene therapy vector for numerous reasons, including its non-pathogenicity and its ability to induce long-term expression of a transgene in multiple target cell types. However, the large scale production of AAV is complex, either involving transient plasmid transfection or co-infection with a helper virus (such as adenovirus), which must eventually be removed from the product to avoid helper-induced pathogenicity. The helper virus approach offers some advantages for large scale production, though adenovirus must then be eliminated during AAV purification. One widely used procedure for inactivating adenovirus following the production phase involves heating to 55 |[deg]|C. However, this procedure results in the concomitant loss of |[sim]|50% of the active AAV particles. Therefore, it may be desirable to develop alternate, rapid, robust, and economical methods that selectively inactivate undesired viruses while leaving AAV particles intact.

RNA interference (RNAi) is a robust and highly evolutionarily conserved mechanism for down-regulating gene expression through targeted mRNA cleavage. It has been shown that RNAi can effectively reduce the expression of human immunodeficiency virus 1 (HIV-1) genes in vitro, but since several nucleotides of non-homology between the siRNA and the viral RNA are sufficient to significantly undermine target degradation, the emergence of resistant strains is a major potential obstacle to long term suppression of this highly mutable virus. We seek to identify quantitative design strategies for using RNAi to suppress HIV-1 in a manner that minimizes the chance of viral escape. In the results presented here, we have focused on RNAi strategies that target the viral TAR loop - a very highly conserved non-coding sequence that participates in a positive feedback loop with the viral early transcription factor, Tat.

Top of pageAbstract RNA interference (RNAi) is an innate cellular process in which double-stranded small interfering RNAs (siRNAs) act as triggers to induce sequence-specific degradation of RNA molecules with which they share homology. RNAi has been shown to effectively reduce the expression of viral genes in vitro, which suggests that it might be especially useful as a genetic therapy to combat or protect against viral pathogens that have RNA genomes, such as human immunodeficiency virus (HIV). However, the RNAi response is so specific that several nucleotides of non-homology between the siRNA and the viral RNA are sufficient to significantly impede target degradation. Since HIV mutates rapidly, the emergence of resistant strains is a major obstacle to potential clinical applications. Using an integrated computational and experimental approach, we are studying HIV evolution in response to RNAi pressure in order to develop quantitative design strategies that delay or prevent viral escape. We constructed a novel stochastic simulation that mechanistically models HIV replication, and we used such simulations to make some counterintuitive predictions about the efficacies of several antiviral RNAi strategies. Design considerations we have investigated computationally include the number of HIV sequences targeted, the uniformity of siRNA delivery, and the consequences of targeting the tat-TAR feedback loop. To test these hypotheses experimentally, we developed a lentiviral siRNA vector that induces sustained and dose-dependent reductions (up to >95%) of target gene expression. With this vector, we can effectively and specifically target highly conserved regions of the HIV genome for RNAi-mediated degradation. This system was used to create stable cell lines that can now serve as tunable RNAi challenges for a replicating HIV model virus. We anticipate that these studies will be useful for identifying antiviral RNAi therapy strategies that most effectively suppress HIV infections.

SummaryHIV can rapidly evolve when placed under selective pressure, including immune surveillance or the administration of antiretroviral drugs. Typically, a variant protein allows HIV to directly evade the selective pressure. Similarly, HIV has escaped suppression by RNA interference (RNAi) directed against viral RNAs by acquiring mutations at the target region that circumvent RNAi-mediated inhibition while conserving necessary viral functions. However, when we directed RNAi against the viral TAR hairpin, which plays an indispensable role in viral transcription, resistant strains were recovered, but none carried a mutation at the target site. Instead, we isolated several strains carrying promoter mutations that indirectly compensated for the RNAi by upregulating viral transcription. Combining RNAi with the application of an antiviral drug blocked replication of such mutants. Evolutionary tuning of viral transcriptional regulation may serve as a general evasion mechanism that may be targeted to improve the efficacy of antiviral therapy.