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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 at the end of RNA transcripts, tracts comprised of multiple uracils are required for Pol III termination, whether upstream RNA secondary structure in the nascent transcript is necessary for robust transcriptional termination is still subject to debate. We sought to address this directly through the development of an in cellulo Pol III transcription termination assay using a synthetic biology approach. Specifically, we utilized the recently developed Tornado expression system and a stabilized Corn RNA aptamer 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. We found that in the absence of predicted secondary structure, only poly-U tracts longer than then the average length found in the human genome (4–5 nucleotides), efficiently terminate Pol III transcription. We found that shorter poly-U tracts could induce termination when placed in proximity to secondary structural elements, while secondary structure by itself was not 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.

Extracellular vesicles (EVs) are nanometer-scale particles that are secreted by cells and mediate intercellular communication by transferring biomolecules between cells. Harnessing this mechanism for therapeutic biomolecule delivery represents a promising frontier for regenerative medicine and other clinical applications. One challenge to realizing this goal is that to date, our understanding of which factors affect EV uptake by recipient cells remains incomplete. In this study, we systematically investigated such delivery questions in the context of breast cancer cells, which are one of the most well-studied cell types with respect to EV delivery and therefore comprise a facile model system for this investigation. By displaying various targeting peptides on the EV surface, we observed that although displaying GE11 on EVs modestly increased uptake by MCF-7 cells, neuropeptide Y (NPY) display had no effect on uptake by the same cells. In contrast, neurotensin (NTS) and urokinase plasminogen activator (uPA) display reduced EV uptake by MDA-MB-231 cells. Interestingly, EV uptake rate did not depend on the source of the EVs; breast cancer cells demonstrated no increase in uptake on administration of breast cancer-derived EVs in comparison to HEK293FT-derived EVs. Moreover, EV uptake was greatly enhanced by delivery in the presence of polybrene and spinoculation, suggesting that maximal EV uptake rates are much greater than those observed under basal conditions in cell culture. By investigating how the cell's environment might provide cues that impact EV uptake, we also observed that culturing cells on soft matrices significantly enhanced EV uptake, compared to culturing on stiff tissue culture polystyrene. Each of these observations provides insights into the factors impacting EV uptake by breast cancer cells, while also providing a basis of comparison for systematically evaluating and perhaps enhancing EV uptake by various cell types.

Engineered cell-based therapies comprise a promising, emerging biomedical technology. Broad utilization of this strategy will require new approaches for implementing sophisticated functional programs, such as sensing and responding to the environment in a defined fashion. Towards this goal, we investigated whether our self-contained receptor and signal transduction system (MESA) could be multiplexed to evaluate extracellular cues, with a focus on elucidating principles governing the integration of such engineered components. We first developed a set of hybrid promoters that exhibited AND gate activation by two transcription factors. We then evaluated these promoters when paired with two MESA receptors and various ligand combinations. Unexpectedly, although the multiplexed system exhibited distinct responses to ligands applied individually and in combination, the same synergy was not observed as when promoters were characterized with soluble transcription factors. Therefore, we developed a mechanistic computational model leveraging these observations, to both improve our understanding of how the receptors and promoters interface and to guide the design and implementation of future systems. Notably, the model explicitly accounts for the impact of intercellular variation on system characterization and performance. Model analysis identified key factors that affect the current receptors and promoters, and enabled an in silico exploration of potential modifications that inform the design of improved logic gates and their robustness to intercellular variation. Ultimately, this quantitative design-driven approach may guide the use and multiplexing of synthetic receptors for diverse custom biological functions beyond the case study considered here.

Efforts to engineer microbial factories have benefitted from mining biological diversity and high throughput synthesis of novel enzymatic pathways, yet screening and optimizing metabolic pathways remain rate-limiting steps. Metabolite-responsive biosensors may help to address these persistent challenges by enabling the monitoring of metabolite levels in individual cells and metabolite-responsive feedback control. We are currently limited to naturally evolved biosensors, which are insufficient for monitoring many metabolites of interest. Thus, a method for engineering novel biosensors would be powerful, yet we lack a generalizable approach that enables the construction of a wide range of biosensors. As a step toward this goal, we here explore several strategies for converting a metabolite-binding protein into a metabolite-responsive transcriptional regulator. By pairing a modular protein design approach with a library of synthetic promoters and applying robust statistical analyses, we identified strategies for engineering biosensor-regulated bacterial promoters and for achieving design-driven improvements of biosensor performance. We demonstrated the feasibility of this strategy by fusing a programmable DNA binding motif (zinc finger module) with a model ligand binding protein (maltose binding protein), to generate a novel biosensor conferring maltose-regulated gene expression. This systematic investigation provides insights that may guide the development of additional novel biosensors for diverse synthetic biology 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.

Extracellular vesicles (EVs) mediate intercellular communication through transfer of RNA and protein between cells. Thus, understanding how cargo molecules are loaded and delivered by EVs is of central importance for elucidating the biological roles of EVs and developing EV-based therapeutics. While some motifs modulating the loading of biomolecular cargo into EVs have been elucidated, the general rules governing cargo loading and delivery remain poorly understood. To investigate how general biophysical properties impact loading and delivery of RNA by EVs, we developed a platform for actively loading engineered cargo RNAs into EVs. In our system, the MS2 bacteriophage coat protein was fused to EV-associated proteins, and the cognate MS2 stem loop was engineered into cargo RNAs. Using this Targeted and Modular EV Loading (TAMEL) approach, we identified a configuration that substantially enhanced cargo RNA loading (up to 6-fold) into EVs. When applied to vesicles expressing the vesicular stomatitis virus glycoprotein (VSVG) – gesicles – we observed a 40-fold enrichment in cargo RNA loading. While active loading of mRNA-length (>1.5 kb) cargo molecules was possible, active loading was much more efficient for smaller (~0.5 kb) RNA molecules. 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 model system. Overall, most cargo was rapidly degraded in recipient cells, despite high EV-loading efficiencies and substantial EV uptake by recipient cells. While gesicles were efficiently internalized via a VSVG-mediated mechanism, most cargo molecules were rapidly degraded. Thus, in this model system, inefficient endosomal fusion or escape likely represents a limiting barrier to EV-mediated transfer. Altogether, the TAMEL platform enabled a comparative analysis elucidating a key opportunity for enhancing EV-mediated delivery to prostate cancer cells, and this technology should be of general utility for investigations and applications of EV-mediated transfer in other systems. Keywords: extracellular vesicles; exosomes; microvesicles; active loading; Lamp2b; VSVG; MS2 coat protein dimer; CD63 (Published: 13 May 2016) Citation: Journal of Extracellular Vesicles 2016, 5: 31027 - http://dx.doi.org/10.3402/jev.v5.31027

Biomaterial scaffolds are central to many regenerative strategies as they create a space for infiltration of host tissue and provide a platform to deliver growth factors and progenitor cells. However, biomaterial implantation results in an unavoidable inflammatory response, which can impair tissue regeneration and promote loss or dysfunction of transplanted cells. We investigated localized TGF-β1 delivery to modulate this immunological environment around scaffolds and transplanted cells. TGF-β1 was delivered from layered scaffolds, with protein entrapped within an inner layer and outer layers designed for cell seeding and host tissue integration. Scaffolds were implanted into the epididymal fat pad, a site frequently used for cell transplantation. Expression of cytokines TNF-α, IL-12, and MCP-1 were decreased by at least 40% for scaffolds releasing TGF-β1 relative to control scaffolds. This decrease in inflammatory cytokine production corresponded to a 60% decrease in leukocyte infiltration. Transplantation of islets into diabetic mice on TGF-β1 scaffolds significantly improved the ability of syngeneic islets to control blood glucose levels within the first week of transplant and delayed rejection of allogeneic islets. Together, these studies emphasize the ability of localized TGF-β1 delivery to modulate the immune response to biomaterial implants and enhance cell function in cell-based therapies.

Macrophage-initiated inflammation is tightly regulated to eliminate threats such as infections while suppressing harmful immune activation. However, individual cells’ signaling responses to pro-inflammatory cues are heterogeneous, with subpopulations emerging with high or low activation states. Here, we use single-cell tracking and dynamical modeling to develop and validate a revised model for lipopolysaccharide (LPS)-induced macrophage activation that invokes a mechanism we term quorum licensing. The results show that bimodal phenotypic partitioning of macrophages is primed during the resting state, dependent on cumulative history of cell density, predicted by extrinsic noise in transcription factor expression, and independent of canonical LPS-induced intercellular feedback in the tumor necrosis factor (TNF) response. Our analysis shows how this density-dependent coupling produces a nonlinear effect on collective TNF production. We speculate that by linking macrophage density to activation, this mechanism could amplify local responses to threats and prevent false alarms., Macrophage activation is tightly regulated to maintain immune homeostasis, yet activation is also heterogeneous. Here, the authors show that macrophages coordinate activation by partitioning into two phenotypes that can nonlinearly amplify collective inflammatory cytokine production as a function of cell density.

In natural microbial systems, conditional spatial sequestration of transcription factors enables cells to respond rapidly to changes in their environment or intracellular state by releasing presynthesized regulatory proteins. Although such a mechanism may be useful for engineering synthetic biology technologies ranging from cell-based biosensors to biosynthetic platforms, to date it remains unknown how or whether such conditional spatial sequestration may be engineered. In particular, based upon seemingly contradictory reports in the literature, it is not clear whether subcellular spatial localization of a transcription factor within the cytoplasm is sufficient to preclude regulation of cognate promoters on plasmid-borne or chromosomal loci. Here, we describe a modular, orthogonal platform for investigating and implementing this mechanism using protease-alleviated spatial sequestration (PASS). In this system, expression of an exogenous protease mediates the proteolytic release of engineered transcriptional regulators from the inner face of the Escherichia coli cytoplasmic membrane. We demonstrate that PASS mediates robust, conditional regulation of either transcriptional repression, via tetR, or transcriptional activation, by the λ phage CI protein. This work provides new insights into a biologically important facet of microbial gene expression and establishes a new strategy for engineering conditional transcriptional regulation for the microbial synthetic biology toolbox.

Extracellular vesicles (EVs) are membrane-enclosed particles that are secreted by nearly all cells and play an important role in intercellular communication by transporting protein and nucleic acids between cells. EV-mediated processes shape phenomena as diverse as cancer progression, immune function, and wound healing. The natural role of EVs in encapsulating and delivering cargo to modify cellular function highlights the potential to use these particles as therapeutic delivery vehicles. In this chapter, we describe emerging strategies for EV engineering and consider how different approaches to EV production, purification, and design may impact the efficacy of EV-based therapeutics.

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.

Many aspects of intercellular communication are mediated through “sending” and “receiving” packets of information via the secretion and subsequent receptor-mediated detection of biomolecular species including cytokines, chemokines, and even metabolites. Recent evidence has now established a new modality of intercellular communication through which biomolecular species are exchanged between cells via extracellular lipid vesicles. A particularly important class of extracellular vesicles is exosomes, which is a term generally applied to biological nanovesicles ~30–200 nm in diameter. Exosomes form through invagination of endosomes to encapsulate cytoplasmic contents, and upon fusion of these multivesicular endosomes to the cell surface, exosomes are released to the extracellular space and transport mRNA, microRNA (miRNA) and proteins between cells. Importantly, exosome-mediated delivery of such cargo molecules results in functional modulation of the recipient cell, and such modulation is sufficiently potent to modulate disease processes in vivo. It is possible that such functional delivery of biomolecules indicates that exosomes utilize native mechanisms (e.g., for internalization and trafficking) that may be harnessed by using exosomes to deliver exogenous RNA for therapeutic applications. A complementary perspective is that understanding the mechanisms of exosome-mediated transport may provide opportunities for “reverse engineering” such mechanisms to improve the performance of synthetic delivery vehicles. In this review, we summarize recent progress in harnessing exosomes for therapeutic RNA delivery, discuss the potential for engineering exosomes to overcome delivery challenges and establish robust technology platforms, and describe both potential challenges and advantages of utilizing exosomes as RNA delivery vehicles.

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as E. coli, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions.

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.

We describe a microarray-based approach for the high-throughput screening of gene function in stem cells and demonstrate the potential of this method by growing and isolating clonal populations of both adult and embryonic neural stem cells. Clonal microarrays are constructed by seeding a population of cells at clonal density on micropatterned surfaces generated using soft lithographic microfabrication techniques. Clones of interest can be isolated after assaying in parallel for various cellular processes and functions, including proliferation, signal transduction, and differentiation. We demonstrate the compatibility of the technique with both gain- and loss-of-function studies using cell populations infected with cDNA libraries or DNA constructs that induce RNA interference. The infection of cells with a library prior to seeding and the compact but isolated growth of clonal cell populations will facilitate the screening of large libraries in a wide variety of mammalian cells, including those that are difficult to transfect by conventional methods. Disclosure of potential conflicts of interest is found at the end of this article.

Engineered cell-based therapies comprise a rapidly growing clinical technology for treating disease by leveraging the natural capabilities of cells, including migration, information transduction, and biosynthesis and secretion. There now exists a substantial portfolio of intracellular and extracellular sensors that enable bioengineers to program cells to execute defined responses to specific changes in state or environmental cues. As our capability to construct more sophisticated cellular programs increases, assessing and improving the degree to which cell-based therapies perform as desired in vivo will become an increasingly important consideration and opportunity for technological advancement. In this review, we seek to describe both current capabilities and potential needs for building cell-based therapies that interface with host physiology in a manner that is robust - a phrase we use in this context to describe the achievement of therapeutic efficacy across a range of patients and implementations. We first review the portfolio of sensors and outputs currently available for use in cell-based therapies by highlighting key advancements and current gaps. Then, we propose a conceptual framework for evaluating and pursuing robust clinical performance of engineered cell-based therapies.

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.

Recently developed antiviral strategies based upon RNA interference (RNAi), which harnesses an innate cellular system for the targeted down-regulation of gene expression, appear highly promising and offer alternative approaches to conventional highly active antiretroviral therapy or efforts to develop an AIDS vaccine. However, RNAi is faced with several challenges that must be overcome to fully realize its promise. Specifically, it degrades target RNA in a highly sequence-specific manner and is thus susceptible to viral mutational escape, and there are also challenges in delivery systems to induce RNAi. To aid in the development of anti-human immunodeficiency virus (anti-HIV) RNAi therapies, we have developed a novel stochastic computational model that simulates in molecular-level detail the propagation of an HIV infection in cells expressing RNAi. The model provides quantitative predictions on how targeting multiple locations in the HIV genome, while keeping the overall RNAi strength constant, significantly improves efficacy. Furthermore, it demonstrates that delivery systems must be highly efficient to preclude leaving reservoirs of unprotected cells where the virus can propagate, mutate, and eventually overwhelm the entire system. It also predicts how therapeutic success depends upon a relationship between RNAi strength and delivery efficiency and uniformity. Finally, targeting an essential viral element, in this case the HIV TAR region, can be highly successful if the RNAi target sequence is correctly selected. In addition to providing specific predictions for how to optimize a clinical therapy, this system may also serve as a future tool for investigating more fundamental questions of viral evolution.

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.

Macrophages functionally polarize towards either immunostimulatory (M1) or immunosuppressive (M2) phenotypes, and tumor-associated macrophages (TAMs) exhibit an M2-like phenotype that impedes immunotherapy. Although the importance of M2 cells in tumor progression is well established, the process by which macrophages “decide” to adopt an M2 phenotype in complex environments is not well understood and is the subject of this study. Most prior investigations have applied “coherent” stimuli (exclusively pro-M1 or pro-M2 signals), yet these stimuli rarely exist independently in vivo. To understand how macrophages integrate incoherent stimuli (both pro-M1 and pro-M2 simultaneously), we first exposed macrophages to dosed combinations of IL-10 and IL-12. Contrary to reports that IL-12 directly alters macrophage polarization state, we observed that when competing directly with IL-10, IL-12 had little direct effect on macrophage polarization. Instead, our data suggest that maintenance of the IL-10-induced M2 state is mediated via the NFκB inhibitor BCL-3, and the M2 state was disrupted by either removal of IL-10 or co-treatment with the pro-M1 stimulus IFNγ. Together, these data suggest that IL-12 may indirectly promote M1 phenotypes by inducing innate and adaptive immune cells to produce IFNγ in the tumor microenvironment. To investigate how interactions between macrophages may influence polarization outcomes, we next examined polarization in individual cells using flow cytometry. Surprisingly, we observed that M1 and M2 cells co-existed in vitro, and that the probability of polarization towards M2 was dose-dependent on IL-10 and independent of IL-12 co-treatment. These data represent the first evidence to date the macrophage polarization is “stochastic” at a coarse-grained level. Heterogeneity was pronounced immediately following activation via LPS and evolved via dynamics that differed based upon cytokine pretreatment. Finally, we investigated macrophage polarization dynamics in vivo. Although conventional wisdom states that macrophages transition from M1 to M2 during tumor progression, this putative transition is poorly understood and sparsely characterized at early stages of tumor development. Therefore, we characterized the dynamics of macrophage polarization during melanoma progression in a syngeneic murine model (B16F0 cells and C57/Bl6 mice). Heterogeneous macrophage polarization was observed at early time points post-tumor implantation, macrophages transitioned from M1 to M2 (systemically and at the tumor) as CD8+ CTL transitioned into regulatory T cell phenotypes. Such insights reshape our understanding of immune responses and should help to identify novel therapeutic targets and strategies for treating cancer.

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 (TLR) ligand-binding domains comprise 18-25 tandem copies of a 24-residue motif known as the leucine-rich repeat (LRR). Unlike other LRR proteins, TLRs contain significant numbers of non-consensus LRR sequences, which makes their identification by computer domain search programs problematic. Here, we provide methods for identifying non-consensus LRRs. Using the location of these LRRs, hypothetical models are constructed based on the known molecular structures of homologous LRR proteins. However, when a hypothetical model for TLR3 is compared with the molecular structure solved by x-ray crystallography, the solenoid curvature, planarity, and conformations of the LRR insertions are incorrectly predicted. These differences illustrate how non-consensus LRR motifs influence TLR structure. Since the determination of molecular structures by crystallography requires substantial amounts of protein, we describe methods for producing milligram amounts of TLR3 extracellular domain (ECD) protein. The recombinant TLR3-ECD previously used to solve the molecular structure of TLR3-ECD has also been used to study the binding of TLR3-ECD to its ligand, double-stranded RNA (dsRNA). In the last section, we describe the preparation of defined TLR3 ligands and present methods for characterizing their interaction with TLR3-ECD.

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.

Toll-like receptors (TLRs) initiate immune responses by recognizing pathogen-associated molecules, but the molecular basis for recognition is poorly understood. In particular, it is unclear how receptor-ligand interactions lead to the initiation of downstream signaling. Here, we describe the mechanism by which TLR3 recognizes its ligand, double-stranded RNA (dsRNA), and forms an active signaling complex. We show that dsRNA binds saturably, specifically, and reversibly to a defined ligand-binding site (or sites) on the TLR3 ectodomain (TLR3ecd). Binding affinities increase with both buffer acidity and ligand size. Purified TLR3ecd protein is exclusively monomeric in solution, but through a highly cooperative process, it forms dimers when bound to dsRNA, and multiple TLR3ecd dimers bind to long dsRNA strands. The smallest dsRNA oligonucleotides that form stable complexes with TLR3ecd (40–50 bp) each bind one TLR3ecd dimer, and these are also the smallest oligonucleotides that efficiently activate TLR3 in cells. We conclude that TLR3 assembles on dsRNA as stable dimers and that the minimal signaling unit is one TLR3 dimer.

International audience; Some Toll and Toll-like receptors (TLRs) provide immunity to experimental infections in animal models, but their contribution to host defense in natural ecosystems is unknown. We report a dominant-negative TLR3 allele in otherwise healthy children with herpes simplex virus 1 (HSV-1) encephalitis. TLR3 is expressed in the central nervous system (CNS), where it is required to control HSV-1, which spreads from the epithelium to the CNS via cranial nerves. TLR3 is also expressed in epithelial and dendritic cells, which apparently use TLR3-independent pathways to prevent further dissemination of HSV-1 and to provide resistance to other pathogens in TLR3-deficient patients. Human TLR3 appears to be redundant in host defense to most microbes but is vital for natural immunity to HSV-1 in the CNS, which suggests that neurotropic viruses have contributed to the evolutionary maintenance of TLR3.

Gene delivery vectors based on adeno-associated virus (AAV) have significant therapeutic potential, but much room for improvement remains in the areas of vector engineering and production. AAV production requires complementation with either helper virus, such as adenovirus, or plasmids containing helper genes, and helper virus-based approaches have distinct advantages in the use of bioreactors to produce large quantities of AAV vectors for clinical applications. However, helper viruses must eventually be inactivated and removed from AAV preparations to ensure safety. The current practice of thermally inactivating adenovirus is problematic as it can also inactivate AAV. Here, we report a novel method using high hydrostatic pressure (HHP) to selectively and completely inactivate helper adenovirus without any detectable loss of functional AAV vectors. The pressure inactivation kinetics of human adenovirus serotype 5 and the high-pressure stabilities of AAV serotypes 2 and 5 (AAV2, AAV5), which were previously unknown, were characterized. Adenovirus was inactivated beyond detection at 260 MPa or higher, whereas AAV2 was stable up to approximately 450 MPa, and surprisingly, AAV5 was stable up to at least 700 MPa. The viral genomic DNA of pressure-inactivated AAV2 was made sensitive to DNAse I digestion, suggesting that gross changes in particle structure had occurred, and this hypothesis was further supported by transmission electron microscopy. This approach should be useful in the laboratory- and clinical-scale production of AAV gene delivery vectors. Moreover, HHP provides a tool for probing the biophysical properties of AAV, which may facilitate understanding and improving the functions of this important virus.

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.