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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.

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.

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.

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.

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.

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.

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.

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.

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.