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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.

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., (© 2024. The Author(s).)

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay. This test uses the isothermal amplification technique NASBA to amplify target viral nucleic acids, followed by Cas13a-based detection of amplified sequences. We first demonstrate an in-house formulation of NASBA that enables optimization of individual NASBA components. We then present design rules for NASBA primer sets and LbuCas13a guide RNAs for fast and sensitive detection of SARS-CoV-2 viral RNA fragments, resulting in 20 - 200 aM sensitivity without any specialized equipment. Finally, we explore the combination of high-throughput assay condition screening with mechanistic ordinary differential equation modeling of the reaction scheme to gain a deeper understanding of the NASBA-Cas13a system. This work presents a framework for developing a mechanistic understanding of reaction performance and optimization that uses both experiments and modeling, which we anticipate will be useful in developing future nucleic acid detection technologies., Competing Interests: CONFLICT OF INTEREST DISCLOSURE K.K.A. M. C. J. & J.B.L. are founders and have financial interest in Stemloop, Inc, and these interests are reviewed and managed by Northwestern University and Stanford University in accordance with their conflict-of-interest policies. All other authors declare no conflicts of interest.

Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity and other properties. Measuring how incorporation varies across a population of EVs is important for characterising such materials and understanding their function, yet it remains challenging to quantitatively characterise the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterisation platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labelling to antibody-labelling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labelling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterisation tool which complements and expands existing methods., (© 2024 The Author(s). Journal of Extracellular Vesicles published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.)

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists., (© 2024. The Author(s).)

As the use of engineered cell therapies expands from pioneering efforts in cancer immunotherapy to other applications, an attractive but less explored approach is the use of engineered red blood cells (RBCs). Compared to other cells, RBCs have a very long circulation time and reside in the blood compartment, so they could be ideally suited for applications as sentinel cells that enable in situ sensing and diagnostics. However, we largely lack tools for converting RBCs into biosensors. A unique challenge is that RBCs remodel their membranes during maturation, shedding many membrane components, suggesting that an RBC-specific approach may be needed. Toward addressing this need, here we develop a biosensing architecture built on RBC membrane proteins that are retained through erythropoiesis. This biosensor employs a mechanism in which extracellular ligand binding is transduced into intracellular reconstitution of a split output protein (including either a fluorophore or an enzyme). By comparatively evaluating a range of biosensor architectures, linker types, scaffold choices, and output signals, we identify biosensor designs and design features that confer substantial ligand-induced signal in vitro . Finally, we demonstrate that erythroid precursor cells engineered with our RBC-protein biosensors function in vivo. This study establishes a foundation for developing RBC-based biosensors that could ultimately address unmet needs including noninvasive monitoring of physiological signals for a range of diagnostic applications.

The genetic modification of T cells has advanced cellular immunotherapies, yet the delivery of biologics specifically to T cells remains challenging. Here we report a suite of methods for the genetic engineering of cells to produce extracellular vesicles (EVs)-which naturally encapsulate and transfer proteins and nucleic acids between cells-for the targeted delivery of biologics to T cells without the need for chemical modifications. Specifically, the engineered cells secreted EVs that actively loaded protein cargo via a protein tag and that displayed high-affinity T-cell-targeting domains and fusogenic glycoproteins. We validated the methods by engineering EVs that delivered Cas9-single-guide-RNA complexes to ablate the gene encoding the C-X-C chemokine co-receptor type 4 in primary human CD4 + T cells. The strategy is amenable to the targeted delivery of biologics to other cell types., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Off-the shelf immune cell therapies are potentially curative and may offer cost and manufacturing advantages over autologous products, 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 cells lose activity post-injection, limiting efficacy. This may be addressed by engineering NK92 cells to express stimulatory factors, and comparative analysis is needed. Thus, we systematically explored the expression of synthetic cytokines for enhancing NK92 cell 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 cell cytotoxicity. Engineered cells were preferentially expanded by expressing membrane-bound but not soluble synthetic cytokines, without compromising the radiosensitivity required for safety. 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 cells., Competing Interests: No competing financial interests exist., (Copyright 2023, Mary Ann Liebert, Inc., publishers.)

We present an educational unit to teach computational modeling, a vital part of chemical engineering curricula, through the lens of synthetic biology. Lectures, code, and homework questions provide conceptual and practical introductions to each computational method involved in the model development process, along with perspectives on how methods can be iterated upon to arrive at a final model. Ultimately, this content can be applied broadly to address questions in synthetic biology and classical chemical engineering.

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., Competing Interests: Declaration of interests J.T. and L.B. acknowledge outside interest in Stylus Medicine., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Prybutok, Yu, Leonard and Bagheri.)

Pramlintide is an equipotent amylin analogue that reduces food intake and body weight in obese subjects and has been clinically approved as an adjunctive therapy for the treatment of adult diabetic patients. However, due to its extremely short half-life in vivo, a regimen of multiple daily administrations is required for achieving clinical effectiveness. Herein is described the development of prototypical long-acting pramlintide bioconjugates, in which pramlintide's disulfide-linked macrocycle was replaced by a cyclic thioether motif. This modification enabled stable chemical conjugation to a half-life extending antibody. In contrast to pramlintide (t 1/2  < 0.75 h), bioconjugates 35 and 38 have terminal half-lives of ∼2 days in mice and attain significant exposure levels that are maintained up to 7 days. Single dose subcutaneous administration of 35 in lean mice, given 18-20 h prior to oral acetaminophen (AAP) administration, significantly reduced gastric emptying (as determined by plasma AAP levels). In a separate study, similar administration of 35 in fasted lean mice effected a reduction in food intake for up to 48 h. These data are consistent with durable amylinomimetic responses and provide the basis for further development of such long-acting amylinomimetic conjugates for the potential treatment of obesity and associated pathologies., (Copyright © 2022 Elsevier Masson SAS. All rights reserved.)

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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Standard metabolic rate (SMR) and maximum metabolic rate (MMR) were determined for Nile tilapia acclimated to six different experimental temperatures from 18 °C to 38 °C. SMR increased exponentially with temperature, from 79.8 mg O 2 kg -1 h -1 at 18 °C, to 255.1 mg O 2 kg -1 h -1 at 38 °C (Q 10  = 1.79). The main increase in Q 10 occurred within the highest temperature range, whereas in the lower temperature from 18 °C to 22 °C, temperature did not significantly affect SMR. MMR showed a hyperbolic correlation with increasing temperature, rising from 240.5 mg O 2 kg -1 h -1 at 18 °C to a peak of 373.8 mg O 2 kg -1 h -1 at 30 °C, before decreasing again at higher temperatures. Absolute aerobic scope (AAS) peaked at 26.0 °C, which we conclude to be the optimal temperature for Nile tilapia. The optimal temperature range, defined as the thermal range where 80% or more of the metabolic scope (MS) can be maintained, occurred between 19.5 and 32.1 °C. The lower (TC MIN ) and upper (TC MAX ) critical temperatures occurred at 13.1 °C and 38.8 °C. Nile tilapia showed a 4-fold scope for increasing ventilation frequency from 24 opercular beats min -1 (OB min -1 ) during SMR at 18 °C, to a maximum of 100 OB min -1 which occurred during MMR at 34 °C. f V during MMR increased with temperature, but above 30 °C became uncoupled with MO 2 , as fish were unable to sustain their rates of oxygen consumption despite a high f V . There was a strong correlation between f V and SMR (r 2  = 0.83) across all temperatures indicating that f V is a good predictor of SMR. However, the correlation between MMR and f V was weak (r 2  = 0.06), due to a strong interacting effect of temperature. When selecting data from the thermal optimum range, a good correlation between f V and MO 2 was obtained (r 2  = 0.74)., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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)., (© 2022. Springer Nature America, Inc.)

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing the potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow, which includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.

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., (© 2021 New York Academy of Sciences.)

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., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

Aim: The aim of this study was to evaluate amino acids as glucagon receptor (GCGR)-specific biomarkers in rodents and cynomolgus monkeys in the presence of agonism of both glucagon-like peptide-1 receptor (GLP1R) and GCGR with a variety of dual agonist compounds., Materials and Methods: Primary hepatocytes, rodents (normal, diet-induced obese and GLP1R knockout) and cynomolgus monkeys were treated with insulin (hepatocytes only), glucagon (hepatocytes and cynomolgus monkeys), the GLP1R agonist, dulaglutide, or a variety of dual agonists with varying GCGR potencies., Results: A long-acting dual agonist, Compound 2, significantly decreased amino acids in both wild-type and GLP1R knockout mice in the absence of changes in food intake, body weight, glucose or insulin, and increased expression of hepatic amino acid transporters. Dulaglutide, or a variant of Compound 2 lacking GCGR agonism, had no effect on amino acids. A third variant with ~31-fold less GCGR potency than Compound 2 significantly decreased amino acids, albeit to a significantly lesser extent than Compound 2. Dulaglutide (with saline infusion) had no effect on amino acids, but an infusion of glucagon dose-dependently decreased amino acids on the background of GLP1R engagement (dulaglutide) in cynomolgus monkeys, as did Compound 2., Conclusions: These results show that amino acids are sensitive and translatable GCGR-specific biomarkers., (© 2020 John Wiley & Sons Ltd.)

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., (© 2020 Wiley-VCH GmbH.)

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., (© The Author(s) 2020. Published by Oxford University Press.)

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.

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.

The long circulating half-life and inherently bivalent architecture of IgGs provide an ideal vehicle for presenting otherwise short-lived G-protein-coupled receptor agonists in a format that enables avidity-driven enhancement of potency. Here, we describe the site-specific conjugation of a dual agonist peptide (an oxyntomodulin variant engineered for potency and in vivo stability) to the complementarity-determining regions (CDRs) of an immunologically silent IgG4. A cysteine-containing heavy chain CDR3 variant was identified that provided clean conjugation to a bromoacetylated peptide without interference from any of the endogenous mAb cysteine residues. The resulting mAb-peptide homodimer has high potency at both target receptors (glucagon receptor, GCGR, and glucagon-like peptide 1 receptor, GLP-1R) driven by an increase in receptor avidity provided by the spatially defined presentation of the peptides. Interestingly, the avidity effects are different at the two target receptors. A single dose of the long-acting peptide conjugate robustly inhibited food intake and decreased body weight in insulin resistant diet-induced obese mice, in addition to ameliorating glucose intolerance. Inhibition of food intake and decrease in body weight was also seen in overweight cynomolgus monkeys. The weight loss resulting from dosing with the bivalently conjugated dual agonist was significantly greater than for the monomeric analog, clearly demonstrating translation of the measured in vitro avidity to in vivo pharmacology.

The gut hormone PYY 3-36 reduces food intake in humans and exhibits at least additive efficacy in combination with GLP-1. However, the utility of PYY analogs as anti-obesity agents has been severely limited by emesis and rapid proteolysis, a profile similarly observed with native PYY 3-36 in obese rhesus macaques. Here, we found that antibody conjugation of a cyclized PYY 3-36 analog achieved high NPY2R selectivity, unprecedented in vivo stability, and gradual infusion-like exposure. These properties permitted profound reduction of food intake when administered to macaques for 23 days without a single emetic event in any animal. Co-administration with the GLP-1 receptor agonist liraglutide for an additional 5 days further reduced food intake with only one animal experiencing a single bout of emesis. This antibody-conjugated PYY analog therefore may enable the long-sought potential of GLP-1/PYY-based combination treatment to achieve robust, well-tolerated weight reduction in obese patients., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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.

Highly stable and stimuli/pH-responsive ultrasmall 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 ultrasmall unilamellar vesicle (uSUV). The usPGNs are stable against fusion and aggregation over several weeks, exhibiting 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 with traditional poly(acrylic acid) systems where the molar mass (M n ) of the polymer chains must exceed 16.5 kDa to achieve stimuli-responsive changes in conformation. They also distinguish the small usPGNs from the much-larger polymer-caged nanobin platform where the Chol-PAA chains must be covalently cross-linked to engender stimuli-responsive behaviors.

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 Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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.

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.

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. Toward 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.

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 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.

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.

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.

Engineered cell-based therapies comprise a promising emerging strategy for treating diverse diseases. Realizing this promise requires new tools for engineering cells to sense and respond to soluble extracellular factors, which provide information about both physiological state and the local environment. Here, we report such a biosensor engineering strategy, leveraging a self-contained receptor-signal transduction system termed modular extracellular sensor architecture (MESA). We developed MESA receptors that enable cells to sense vascular endothelial growth factor (VEGF) and, in response, secrete interleukin 2 (IL-2). By implementing these receptors in human T cells, we created a customized function not observed in nature-an immune cell that responds to a normally immunosuppressive cue (VEGF) by producing an immunostimulatory factor (IL-2). Because this platform utilizes modular, engineerable domains for ligand binding (antibodies) and output (programmable transcription factors based upon Cas9), this approach may be readily extended to novel inputs and outputs. This generalizable approach for rewiring cellular functions could enable both translational applications and fundamental biological research.

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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., (© 2016 Wiley Periodicals, Inc.)