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Autologous immune cell therapies are potentially curative, but cost and manufacturing complexity limit access. Off-the-shelf therapies may address these gaps, but further development is needed. The NK92 cell line has a natural killer-like phenotype, has efficacy in cancer clinical trials, and is safe after irradiation. However, NK92 lose activity post-injection, limiting efficacy. This may be addressed by engineering NK92 to express stimulatory factors, for which comparative analysis is needed. Thus, we systematically explored expression of synthetic cytokines for enhancing NK92 production and performance. All synthetic cytokines evaluated (membrane-bound IL2 and IL15, and engineered versions of Neoleukin-2/15, IL15, IL12, and decoy resistant IL18) enhanced NK92 cytotoxicity. Genetically modified cells preferentially expanded by expressing membrane-bound but not soluble synthetic cytokines, and all engineered cells remained sensitive to irradiation. Interestingly, some membrane-bound cytokines conferred cell-contact independent paracrine activity partly attributable to extracellular vesicles. Finally, we characterized interactions within consortia of differently engineered NK92.

Naturally generated lipid nanoparticles termed extracellular vesicles (EVs) hold significant promise as engineerable therapeutic delivery vehicles. However, active loading of protein cargo into EVs in a manner that is useful for delivery remains a challenge. Here, we demonstrate that by rationally designing proteins to traffic to the plasma membrane and associate with lipid rafts, we can enhance loading of protein cargo into EVs for a set of structurally diverse transmembrane and peripheral membrane proteins. We then demonstrate the capacity of select lipid tags to mediate increased EV loading and functional delivery of an engineered transcription factor to modulate gene expression in target cells. We envision that this technology could be leveraged to develop new EV-based therapeutics that deliver a wide array of macromolecular cargo.

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).

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.

Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we develop the COmposable Mammalian Elements of Transcription (COMET)—an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not, to the best of our knowledge, previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells., Engineering mammalian cellular functions requires a toolkit of orthogonal and well-characterized genetic components. Here the authors develop COMET: an ensemble of transcription factors, promoters, and accompanying models for the design and construction of genetic programs.

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.

Synthetic biology has the potential to transform cell- and gene-based therapies for a variety of diseases. Sophisticated tools are now available for both eukaryotic and prokaryotic cells to engineer cells to selectively achieve therapeutic effects in response to one or more disease-related signals, thus sparing healthy tissue from potentially cytotoxic effects. This report summarizes the Keystone eSymposium "Synthetic Biology: At the Crossroads of Genetic Engineering and Human Therapeutics," which took place on May 3 and 4, 2021. Given that several therapies engineered using synthetic biology have entered clinical trials, there was a clear need for a synthetic biology symposium that emphasizes the therapeutic applications of synthetic biology as opposed to the technical aspects. Presenters discussed the use of synthetic biology to improve T cell, gene, and viral therapies, to engineer probiotics, and to expand upon existing modalities and functions of cell-based therapies.

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.

A suite of genetic components and computational models enables the predictive design of mammalian cellular functions., Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

Computational modeling empowers systems biologists to interrogate and understand increasingly complex biological phenomena, and the growing suite of computational approach presents both opportunities and challenges. Choosing the right computational approaches to address a given question requires managing a model's complexity, balancing goals and limitations including interpretability, data resolution, and computational cost. Excess model complexity can diminish the utility for building understanding, while excess simplicity can render the model insufficient for addressing the questions of interest. Using systems immunology as a case study, we review how different model design strategies uniquely manage complexity, ending with a consideration of composite models, which combine the benefits of individual paradigms but present additional challenges arising from added layers of complexity. We anticipate that considering general model design challenges and potential solutions through the lens of complexity will foster enhanced collaboration among computational and experimental researchers.

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.

Introducing exogenous molecules into cells with high efficiency and dosage control is a crucial step in basic research as well as clinical applications. Here, the capability of the nanofountain probe electroporation (NFP-E) system to deliver proteins and plasmids in a variety of continuous and primary cell types with appropriate dosage control is reported. It is shown that the NFP-E can achieve fine control over the relative expression of two cotransfected plasmids. Finally, the dynamics of electropore closure after the pulsing ends with the NFP-E is investigated. Localized electroporation has recently been utilized to demonstrate the converse process of delivery (sampling), in which a small volume of the cytosol is retrieved during electroporation without causing cell lysis. Single-cell temporal sampling confers the benefit of monitoring the same cell over time and can provide valuable insights into the mechanisms underlying processes such as stem cell differentiation and disease progression. NFP-E parameters that maximize the membrane resealing time, which is essential for increasing the sampled volume and in meeting the challenge of monitoring low copy number biomarkers, are identified. Its application in CRISPR/Cas9 gene editing, stem cell reprogramming, and single-cell sampling studies is envisioned.

Synthetic receptors are powerful tools for engineering mammalian cell-based devices. These biosensors enable cell-based therapies to perform complex tasks such as regulating therapeutic gene expression in response to sensing physiological cues. Although multiple synthetic receptor systems now exist, many aspects of receptor performance are poorly understood. In general, it would be useful to understand how receptor design choices influence performance characteristics. In this study, we examined the modular extracellular sensor architecture (MESA) and systematically evaluated previously unexamined design choices, yielding substantially improved receptors. A key finding that might extend to other receptor systems is that the choice of transmembrane domain (TMD) is important for generating high-performing receptors. To provide mechanistic insights, we adopted and employed a Förster resonance energy transfer-based assay to elucidate how TMDs affect receptor complex formation and connected these observations to functional performance. To build further insight into these phenomena, we developed a library of new MESA receptors that sense an expanded set of ligands. Based upon these explorations, we conclude that TMDs affect signaling primarily by modulating intracellular domain geometry. Finally, to guide the design of future receptors, we propose general principles for linking design choices to biophysical mechanisms and performance characteristics.

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.

Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering — which is used to confer and balance properties such as stability and efficiency — with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.

The engineering of cells as programmable devices has enabled therapeutic strategies that could not otherwise be achieved. Such strategies include recapitulating and enhancing native cellular functions and composing novel functions. These novel functions may be composed using both natural and engineered biological components, with the latter exemplified by the development of synthetic receptor and signal transduction systems. Recent advances in implementing these approaches include the treatment of cancer, where the most clinical progress has been made to date, and the treatment of diabetes. Principles for engineering cell-based therapies that are safe and effective are increasingly needed and beginning to emerge, and will be essential in the development of this new class of therapeutics.

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.

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.

Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we developed the COmposable Mammalian Elements of Transcription (COMET)—an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells.

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.

Our previously developed peptide-design algorithm was improved by adding an energy minimization strategy which allows the amino acid sidechains to move in a broad configuration space during sequence evolution. In this work, the new algorithm was used to generate a library of 21-mer peptides which could substitute for λ N peptide in binding to boxB RNA. Six potential peptides were obtained from the algorithm, all of which exhibited good binding capability with boxB RNA. Atomistic molecular dynamics simulations were then conducted to examine the ability of the λ N peptide and three best evolved peptides, viz. Pept01, Pept26, and Pept28, to bind to boxB RNA. Simulation results demonstrated that our evolved peptides are better at binding to boxB RNA than the λ N peptide. Sequence searches using the old (without energy minimization strategy) and new (with energy minimization strategy) algorithms confirm that the new algorithm is more effective at finding good RNA-binding peptides than the old algorithm. © 2016 Wiley Periodicals, Inc.

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.

The ability to engineer custom cell-contact-sensing output devices into human non-immune cells would be useful for extending the applicability of cell-based cancer therapies and avoiding risks associated with engineered immune cells. Here, we have developed a new class of synthetic T-cell receptor-like signal-transduction device that functions efficiently in human non-immune cells and triggers release of output molecules specifically upon sensing contact with a target cell. This device employs an interleukin signaling cascade, whose OFF/ON switching is controlled by biophysical segregation of a transmembrane signal-inhibitory protein from the sensor cell/target cell interface. We further showed that designer non-immune cells equipped with this device driving expression of a membrane-penetrator/prodrug-activating enzyme construct could specifically kill target cells in the presence of the prodrug, indicating its potential usefulness for target-cell-specific, cell-based enzyme-prodrug cancer therapy. Our study also contributes to advancement of synthetic biology by extending available design principles to transmit extracellular information to cells.

Highly stable and stimuli/pH-responsive ultra-small polymer-grafted nanobins (usPGNs) have been developed by grafting a small amount (10 mol %) of short (4.3 kDa) cholesterol-terminated poly(acrylic acid) (Chol-PAA) into an ultra-small unilamellar vesicle (uSUV). The usPGNs are stable against fusion and aggregation over several weeks and exhibit over 10-fold enhanced cargo retention in biologically relevant media at pH 7.4 in comparison with the parent uSUV template. Coarse-grained molecular dynamics (CGMD) simulations confirm that the presence of the cholesterol moiety can greatly stabilize the lipid bilayer. They also show extended PAA chain conformations that can be interpreted as causing repulsion between colloidal particles, thus stabilizing them against fusion. Notably, CGMD predicted a clustering of the Chol-PAA chains on the lipid bilayer under acidic conditions due to intra- and interchain hydrogen bonding, leading to the destabilization of local membrane areas. This explains the experimental observation that usPGNs can be triggered to release a significant amount of cargo upon acidification to pH 5. These developments put the lipid-bilayer-embedded Chol-PAA in stark contrast to traditional poly(acrylic acid) systems where the molar mass (M(n)) of the polymer chains must exceed 16.5 kDa to achieve stimuli-responsive conformation changes. They also distinguish the small usPGNs from the much-larger polymer-caged nanobin (PCN) platform where the Chol-PAA chains must be covalently crosslinked to engender stimuli-responsive behaviors.

Extracellular vesicles (EVs) are secreted nanoscale particles that transfer biomolecular cargo between cells in multicellular organisms. EVs play a variety of roles in intercellular communication and are being explored as potential vehicles for delivery of therapeutic biomolecules. However, EVs are highly heterogeneous in composition and biogenesis route, and this poses substantial challenges for understanding the role of EVs in biology and for harnessing these mechanisms for therapeutic applications, for which purifying therapeutic EVs from mixed EV populations may be necessary. Currently, technologies for isolating EV subsets are limited by overlapping physical properties among EV subsets. To meet this need, here we report an affinity chromatography-based method for enriching a specific EV subset from a heterogeneous EV starting population. By displaying an affinity tagged protein (tag-protein) on the EV surface, tagged EVs may be specifically isolated using simple affinity chromatography. Moreover, recovered EVs are enriched in the tag-protein relative to the starting population of EVs and relative to EVs purified from cell culture supernatant by standard differential centrifugation. Furthermore, chromatographically enriched EVs confer enhanced delivery of a cargo protein to recipient cells (via enhancing the amount of cargo protein per EV) relative to EVs isolated by centrifugation. Altogether, affinity chromatographic enrichment of EV subsets is a viable and facile strategy for investigating EV biology and for harnessing EVs for therapeutic applications.

Extracellular vesicles (EVs) are secreted nanoscale particles that transfer biomolecular cargo between cells in multicellular organisms. EVs play a variety of roles in intercellular communication and are being explored as potential vehicles for delivery of therapeutic biomolecules. However, EVs are highly heterogeneous in composition and biogenesis route, and this poses substantial challenges for understanding the role of EVs in biology and for harnessing these mechanisms for therapeutic applications, for which purifying therapeutic EVs from mixed EV populations may be necessary. Currently, technologies for isolating EV subsets are limited by overlapping physical properties among EV subsets. To meet this need, here we report an affinity chromatography-based method for enriching a specific EV subset from a heterogeneous EV starting population. By displaying an affinity tagged protein (tag-protein) on the EV surface, tagged EVs may be specifically isolated using simple affinity chromatography. Moreover, recovered EVs are enriched in the tag-protein relative to the starting population of EVs and relative to EVs purified from cell culture supernatant by standard differential centrifugation. Furthermore, chromatographically enriched EVs confer enhanced delivery of a cargo protein to recipient cells (via enhancing the amount of cargo protein per EV) relative to EVs isolated by centrifugation. Altogether, affinity chromatographic enrichment of EV subsets is a viable and facile strategy for investigating EV biology and for harnessing EVs for therapeutic applications.

Extracellular RNA (exRNA) has emerged as an important transducer of intercellular communication. Advancing exRNA research promises to revolutionize biology and transform clinical practice. Recent efforts have led to cutting-edge research and expanded knowledge of this new paradigm in cell-to-cell crosstalk; however, gaps in our understanding of EV heterogeneity and exRNA diversity pose significant challenges for continued development of exRNA diagnostics and therapeutics. To unravel this complexity, the NIH convened expert teams to discuss the current state of the science, define the significant bottlenecks, and brainstorm potential solutions across the entire exRNA research field. The NIH Strategic Workshop on Extracellular RNA Transport helped identify mechanistic and clinical research opportunities for exRNA biology and provided recommendations on high priority areas of research that will advance the exRNA field.

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.

To enhance the sampling efficiency of our computational peptide-design algorithm in conformational space, the concerted rotation (CONROT) technique is extended to enable larger conformational perturbations of peptide chains. This allows us to make relatively large peptide conformation changes during the process of designing peptide sequences to bind with high affinity to a specific target. Searches conducted using the new algorithm identified six potential λ N(2-22) peptide variants, called B1-B6, which bind to boxB RNA with high affinity. The results of explicit-solvent atomistic molecular dynamics simulations revealed that four of the evolved peptides, viz. B1, B2, B3, and B5, are excellent candidate binders to the target boxB RNA as they have lower binding free energies than the original λ N(2-22) peptide. Three of the four peptides, B2, B3, and B5, result from searches that contain both sequence and conformation changes, indicating that adding backbone motif changes to the peptide-design algorithm improves its performance considerably.

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.

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.

Mammalian synthetic biology represents a vibrant and growing technical discipline. As this nascent field matures, technologies for engineering sophisticated mammalian cell functions are enabling the development of novel therapies that address unmet medical needs. These transformative capabilities are likely to impact clinical practice in ways that have yet to be identified.