An externally tunable bacterial band-pass filter

Inteins: A Swiss army knife for synthetic biology

Inteins are proteins found in nature that execute protein splicing. Among them, split inteins stand out for their versatility and adaptability, presenting creative solutions for addressing intricate challenges in various biological applications. Their exquisite attributes, including compactness, reliability, orthogonality, low toxicity, and irreversibility, make them of interest to various fields including synthetic biology, biotechnology and biomedicine. In this review, we delve into the inherent challenges of using inteins, present approaches for overcoming these challenges, and detail their reliable use for specific cellular tasks. We will discuss the use of conditional inteins in areas like cancer therapy, drug screening, patterning, infection treatment, diagnostics and biocontainment. Additionally, we will underscore the potential of inteins in executing basic logical operations with practical implications. We conclude by showcasing their potential in crafting complex genetic circuits for performing computations and feedback control that achieves robust perfect adaptation.

Protein-based bandpass filters for controlling cellular signaling with chemical inputs

Biological signal processing is vital for cellular function. Similar to electronic circuits, cells process signals via integrated mechanisms. In electronics, bandpass filters transmit frequencies with defined ranges, but protein-based counterparts for controlled responses are lacking in engineered biological systems. Here, we rationally design protein-based, chemically responsive bandpass filters (CBPs) showing OFF-ON-OFF patterns that respond to chemical concentrations within a specific range and reject concentrations outside that range. Employing structure-based strategies, we designed a heterodimeric construct that dimerizes in response to low concentrations of a small molecule (ON), and dissociates at high concentrations of the same molecule (OFF). The CBPs have a multidomain architecture in which we used known drug receptors, a computationally designed protein binder and small-molecule inhibitors. This modular system allows fine-tuning for optimal performance in terms of bandwidth, response, cutoff and fold changes. The CBPs were used to regulate cell surface receptor signaling pathways to control cellular activities in engineered cells.

Automated design of gene circuits with optimal mushroom-bifurcation behavior

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation, and generic multifunctional biocircuits. These novel applications require the design of gene circuits leading to sophisticated behaviors and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory addresses such challenges by associating circuit dynamical properties with molecular details of its design. Nevertheless, incorporating bifurcation analysis into automated design processes has not been accomplished yet. This work presents an optimization-based method for the automated design of synthetic gene circuits with specified bifurcation diagrams that employ minimal network topologies. Using this approach, we designed circuits exhibiting the mushroom bifurcation, distilled the most robust topologies, and explored its multifunctional behavior. We then outline potential applications in biosensors, memory devices, and synthetic cell differentiation.

Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations

Mutations can have deleterious fitness effects when they decrease protein specific activity or decrease active protein abundance. Mutations will also be deleterious when they cause misfolding or misinteractions that are toxic to the cell (i.e., independent of whether the mutations affect specific activity and abundance). The extent to which protein evolution is shaped by these and other collateral fitness effects is unclear in part because little is known of their frequency and magnitude. Using deep mutational scanning (DMS), we previously found at least 42% of missense mutations in the TEM-1 β-lactamase antibiotic resistance gene cause deleterious collateral fitness effects. Here, we used DMS to comprehensively determine the collateral fitness effects of missense mutations in three genes encoding the antibiotic resistance proteins New Delhi metallo-β-lactamase (NDM-1), chloramphenicol acetyltransferase I (CAT-I), and 2″-aminoglycoside nucleotidyltransferase (AadB). AadB (20%), CAT-I (0.9%), and NDM-1 (0.2%) were less susceptible to deleterious collateral fitness effects than TEM-1 (42%) indicating that genes have different propensities for these effects. As was observed with TEM-1, all the studied deleterious aadB mutants increased aggregation. However, aggregation did not correlate with collateral fitness effects for many of the deleterious mutants of CAT-I and NDM-1. Select deleterious mutants caused unexpected phenotypes to emerge. The introduction of internal start codons in CAT-1 caused loss of the episome and a mutation in aadB made its cognate antibiotic essential for growth. Our study illustrates how the complexity of the cell provides a rich environment for collateral fitness effects and new phenotypes to emerge.

Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops

The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary. One aspect of this is that local network motif analysis of real GRNs indicates that the feed-forward loop (FFL) is significantly enriched. To simulate this properly, we developed a novel motif-based preferential attachment algorithm, FFLatt, which outperformed the popular GeneNetWeaver network generation tool in reproducing the FFL motif occurrence observed in literature-based biological GRNs. It also preserves important topological properties such as scale-free topology, sparsity, and average in/out-degree per node. We conclude that FFLatt is well-suited as a network generation module for a benchmarking framework with the aim to provide fair and robust performance evaluation of GRN inference methods.

Biological signal processing filters via engineering allosteric transcription factors

As the size and complexity of genetic circuits increases, scientists and engineers need to find solutions to rapidly optimize flux and reduce the metabolic burden imposed on chassis cells. In this study, we report synthetic biology tools that imbue chassis cells with advanced signal processing functions akin to electrical devices commonly used in wireless transmitters and receivers (i.e., biological BANDPASS and BANDSTOP devices) that can simultaneously reduce metabolic burden. Moreover, this study presents a set of granular and more complete design rules for engineering allosteric transcription factors in the broader LacI/GalR topology. In addition, this study has improved our fundamental understanding of the plasticity and continuum of allosteric communication from the binding pocket to the protein–DNA interaction.

Signal processing is critical to a myriad of biological phenomena (natural and engineered) that involve gene regulation. Biological signal processing can be achieved by way of allosteric transcription factors. In canonical regulatory systems (e.g., the lactose repressor), an INPUT signal results in the induction of a given transcription factor and objectively switches gene expression from an OFF state to an ON state. In such biological systems, to revert the gene expression back to the OFF state requires the aggressive dilution of the input signal, which can take 1 or more d to achieve in a typical biotic system. In this study, we present a class of engineered allosteric transcription factors capable of processing two-signal INPUTS, such that a sequence of INPUTS can rapidly transition gene expression between alternating OFF and ON states. Here, we present two fundamental biological signal processing filters, BANDPASS and BANDSTOP, that are regulated by D-fucose and isopropyl-β-D-1-thiogalactopyranoside. BANDPASS signal processing filters facilitate OFF–ON–OFF gene regulation. Whereas, BANDSTOP filters facilitate the antithetical gene regulation, ON–OFF–ON. Engineered signal processing filters can be directed to seven orthogonal promoters via adaptive modular DNA binding design. This collection of signal processing filters can be used in collaboration with our established transcriptional programming structure. Kinetic studies show that our collection of signal processing filters can switch between states of gene expression within a few minutes with minimal metabolic burden—representing a paradigm shift in general gene regulation.

Low-N protein engineering with data-efficient deep learning

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.

Generation of extracellular morphogen gradients: the case for diffusion

Cells within developing tissues rely on morphogens to assess positional information. Passive diffusion is the most parsimonious transport model for long-range morphogen gradient formation but does not, on its own, readily explain scaling, robustness and planar transport. Here, we argue that diffusion is sufficient to ensure robust morphogen gradient formation in a variety of tissues if the interactions between morphogens and their extracellular binders are considered. A current challenge is to assess how the affinity for extracellular binders, as well as other biophysical and cell biological parameters, determines gradient dynamics and shape in a diffusion-based transport system. Technological advances in genome editing, tissue engineering, live imaging and in vivo biophysics are now facilitating measurement of these parameters, paving the way for mathematical modelling and a quantitative understanding of morphogen gradient formation and modulation.

A guide to the design of synthetic gene networks in mammalian cells

Synthetic biology aims to harness natural and synthetic biological parts and engineering them in new combinations and systems, producing novel therapies, diagnostics, bioproduction systems, and providing information on the mechanism of function of biological systems. Engineering cell function requires the rewiring or de novo construction of cell information processing networks. Using natural and synthetic signal processing elements, researchers have demonstrated a wide array of signal sensing, processing and propagation modules, using transcription, translation, or post-translational modification to program new function. The toolbox for synthetic network design is ever-advancing and has still ample room to grow. Here, we review the diversity of synthetic gene networks, types of building modules, techniques of regulation, and their applications.

Exploiting natural chemical photosensitivity of anhydrotetracycline and tetracycline for dynamic and setpoint chemo-optogenetic control

The transcriptional inducer anhydrotetracycline (aTc) and the bacteriostatic antibiotic tetracycline (Tc) are commonly used in all fields of biology for control of transcription or translation. A drawback of these and other small molecule inducers is the difficulty of their removal from cell cultures, limiting their application for dynamic control. Here, we describe a simple method to overcome this limitation, and show that the natural photosensitivity of aTc/Tc can be exploited to turn them into highly predictable optogenetic transcriptional- and growth-regulators. This new optogenetic class uniquely features both dynamic and setpoint control which act via population-memory adjustable through opto-chemical modulation. We demonstrate this method by applying it for dynamic gene expression control and for enhancing the performance of an existing optogenetic system. We then expand the utility of the aTc system by constructing a new chemical bandpass filter that increases its aTc response range. The simplicity of our method enables scientists and biotechnologists to use their existing systems employing aTc/Tc for dynamic optogenetic experiments without genetic modification.

Antibiotic Degradation by Commensal Microbes Shields Pathogens

The complex bacterial populations that constitute the gut microbiota can harbor antibiotic resistance genes (ARGs), including those encoding β-lactamase enzymes (BLA), which degrade commonly prescribed antibiotics such as ampicillin. The prevalence of such genes in commensal bacteria has been increased in recent years by the wide use of antibiotics in human populations and in livestock. While transfer of ARGs between bacterial species has well-established dramatic public health implications, these genes can also function in trans within bacterial consortia, where antibiotic-resistant bacteria can provide antibiotic-sensitive neighbors with leaky protection from drugs, as shown both in vitro and in vivo, in models of lung and subcutaneous coinfection. However, whether the expression of ARGs by harmless commensal bacterial species can destroy antibiotics in the intestinal lumen and shield antibiotic-sensitive pathogens is unknown. To address this question, we colonized germfree or wild-type mice with a model intestinal commensal strain of Escherichia coli that produces either functional or defective BLA. Mice were subsequently infected with Listeria monocytogenes or Clostridioides difficile, followed by treatment with oral ampicillin. The production of functional BLA by commensal E. coli markedly reduced clearance of these pathogens and enhanced systemic dissemination during ampicillin treatment. Pathogen resistance was independent of ARG acquisition via horizontal gene transfer but instead relied on antibiotic degradation in the intestinal lumen by BLA. We conclude that commensal bacteria that have acquired ARGs can mediate shielding of pathogens from the bactericidal effects of antibiotics.

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

Automated Design of Pluripotent Stem Cell Self-Organization

Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Fitness Effects of Single Amino Acid Insertions and Deletions in TEM-1 β-Lactamase

Short insertions and deletions (InDels) are a common type of mutation found in nature and a useful source of variation in protein engineering. InDel events have important consequences in protein evolution, often opening new pathways for adaptation. However, much less is known about the effects of InDels compared to point mutations and amino acid substitutions. In particular, deep mutagenesis studies on the distribution of fitness effects of mutations have focused almost exclusively on amino acid substitutions. Here, we present a near-comprehensive analysis of the fitness effects of single amino acid InDels in TEM-1 β-lactamase. While we found InDels to be largely deleterious, partially overlapping deletion-tolerant and insertion-tolerant regions were observed throughout the protein, especially in unstructured regions and at the end of helices. The signal sequence of TEM-1 tolerated InDels more than the mature protein. Most regions of the protein tolerated insertions more than deletions, but a few regions tolerated deletions more than insertions. We examined the relationship between InDel tolerance and a variety of measures to help understand its origin. These measures included evolutionary variation in β-lactamases, secondary structure identity, tolerance to amino acid substitutions, solvent accessibility, and side-chain weighted contact number. We found secondary structure, weighted contact number, and evolutionary variation in class A beta-lactamases to be the somewhat predictive of InDel fitness effects.

Pervasive Pairwise Intragenic Epistasis among Sequential Mutations in TEM-1 β-Lactamase

Interactions between mutations play a central role in shaping the fitness landscape, but a clear picture of intragenic epistasis has yet to emerge. To further reveal the prevalence and patterns of intragenic epistasis, we present a survey of epistatic interactions between sequential mutations in TEM-1 β-lactamase. We measured the fitness effect of ~12,000 pairs of consecutive amino acid substitutions and used our previous study of the fitness effects of single amino acid substitutions to calculate epistasis for over 8000 mutation pairs. Since sequential mutations are prone to physically interact, we postulated that our study would be surveying specific epistasis instead of nonspecific epistasis. We found widespread negative epistasis, especially in beta-strands, and a high frequency of negative sign epistasis among individually beneficial mutations. Negative epistasis (52%) occurred 7.6 times as frequently as positive epistasis (6.8%). Buried residues experienced more negative epistasis that surface-exposed residues. However, TEM-1 exhibited a couple of hotspots for positive epistasis, most notably L221/ R222 at which many combinations of mutations positively interacted. This study is the first to systematically examine pairwise epistasis throughout an entire protein performing its native function in its native host.

Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch

Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks.

Selection of Protein-Protein Interactions of Desired Affinities with a Bandpass Circuit

We have developed a genetic circuit in Escherichia coli that can be used to select for protein-protein interactions of different strengths by changing antibiotic concentrations in the media. The genetic circuit links protein-protein interaction strength to β-lactamase activity while simultaneously imposing tuneable positive and negative selection pressure for β-lactamase activity. Cells only survive if they express interacting proteins with affinities that fall within set high- and low-pass thresholds; i.e. the circuit therefore acts as a bandpass filter for protein-protein interactions. We show that the circuit can be used to recover protein-protein interactions of desired affinity from a mixed population with a range of affinities. The circuit can also be used to select for inhibitors of protein-protein interactions of defined strength.

Converting a Periplasmic Binding Protein into a Synthetic Biosensing Switch through Domain Insertion

All biosensing platforms rest on two pillars: specific biochemical recognition of a particular analyte and transduction of that recognition into a readily detectable signal. Most existing biosensing technologies utilize proteins that passively bind to their analytes and therefore require wasteful washing steps, specialized reagents, and expensive instruments for detection. To overcome these limitations, protein engineering strategies have been applied to develop new classes of protein-based sensor/actuators, known as protein switches, responding to small molecules. Protein switches change their active state (output) in response to a binding event or physical signal (input) and therefore show a tremendous potential to work as a biosensor. Synthetic protein switches can be created by the fusion between two genes, one coding for a sensor protein (input domain) and the other coding for an actuator protein (output domain) by domain insertion. The binding of a signal molecule to the engineered protein will switch the protein function from an “off” to an “on” state (or vice versa) as desired. The molecular switch could, for example, sense the presence of a metabolite, pollutant, or a biomarker and trigger a cellular response. The potential sensing and response capabilities are enormous; however, the recognition repertoire of natural switches is limited. Thereby, bioengineers have been struggling to expand the toolkit of molecular switches recognition repertoire utilizing periplasmic binding proteins (PBPs) as protein-sensing components. PBPs are a superfamily of bacterial proteins that provide interesting features to engineer biosensors, for instance, immense ligand-binding diversity and high affinity, and undergo large conformational changes in response to ligand binding. The development of these protein switches has yielded insights into the design of protein-based biosensors, particularly in the area of allosteric domain fusions. Here, recent protein engineering approaches for expanding the versatility of protein switches are reviewed, with an emphasis on studies that used PBPs to generate novel switches through protein domain insertion.

Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors

Although the structure of a genetically encoded regulatory circuit is an important determinant of its function, the relationship between circuit topology and the dynamical behaviors it can exhibit is not well understood. Here, we explore the range of behaviors available to the AC-DC circuit. This circuit consists of three genes connected as a combination of a toggle switch and a repressilator. Using dynamical systems theory, we show that the AC-DC circuit exhibits both oscillations and bistability within the same region of parameter space; this generates emergent behaviors not available to either the toggle switch or the repressilator alone. The AC-DC circuit can switch on oscillations via two distinct mechanisms, one of which induces coherence into ensembles of oscillators. In addition, we show that in the presence of noise, the AC-DC circuit can behave as an excitable system capable of spatial signal propagation or coherence resonance. Together, these results demonstrate how combinations of simple motifs can exhibit multiple complex behaviors.

Reconfigurable Analog Signal Processing by Living Cells

Living cells are known for their capacity for versatile signal processing, particularly the ability to respond differently to the same stimuli using biochemical networks that integrate environmental signals and reconfigure their dynamic responses. However, the complexity of natural biological networks confounds the discovery of fundamental mechanisms behind versatile signaling. Here, we study one specific aspect of reconfigurable signal processing in which a minimal biological network integrates two signals, using one to reconfigure the network's transfer function with respect to the other, producing an emergent switch between induction and repression. In contrast to known mechanisms, the new mechanism reconfigures transfer functions through genetic networks without extensive protein-protein interactions. These results provide a novel explanation for the versatility of genetic programs, and suggest a new mechanism of signal integration that may govern flexibility and plasticity of gene expression.

Hierarchical control of enzymatic actuators using DNA-based switchable memories

Inspired by signaling networks in living cells, DNA-based programming aims for the engineering of biochemical networks capable of advanced regulatory and computational functions under controlled cell-free conditions. While regulatory circuits in cells control downstream processes through hierarchical layers of signal processing, coupling of enzymatically driven DNA-based networks to downstream processes has rarely been reported. Here, we expand the scope of molecular programming by engineering hierarchical control of enzymatic actuators using feedback-controlled DNA-circuits capable of advanced regulatory dynamics. We developed a translator module that converts signaling molecules from the upstream network to unique DNA strands driving downstream actuators with minimal retroactivity and support these findings with a detailed computational analysis. We show our modular approach by coupling of a previously engineered switchable memories circuit to downstream actuators based on β-lactamase and luciferase. To the best of our knowledge, our work demonstrates one of the most advanced DNA-based circuits regarding complexity and versatility.

Naturally evolved regulatory circuits have hierarchical layers of signal generation and processing. Here, the authors emulate these networks using feedback-controlled DNA circuits that convert upstream signaling to downstream enzyme activity in a switchable memories circuit.

Using synthetic biology to explore principles of development

Developmental biology is mainly analytical: researchers study embryos, suggest hypotheses and test them through experimental perturbation. From the results of many experiments, the community distils the principles thought to underlie embryogenesis. Verifying these principles, however, is a challenge. One promising approach is to use synthetic biology techniques to engineer simple genetic or cellular systems that follow these principles and to see whether they perform as expected. As I review here, this approach has already been used to test ideas of patterning, differentiation and morphogenesis. It is also being applied to evo-devo studies to explore alternative mechanisms of development and 'roads not taken' by natural evolution.

Understanding and engineering plant form

A plant's form is an important determinant of its fitness and economic value. Here, we review strategies for producing plants with altered forms. Historically, the process of changing a plant's form has been slow in agriculture, requiring iterative rounds of growth and selection. We discuss modern techniques for identifying genes involved in the development of plant form and tools that will be needed to effectively design and engineer plants with altered forms. Synthetic genetic circuits are highlighted for their potential to generate novel plant forms. We emphasize understanding development as a prerequisite to engineering and discuss the potential role of computer models in translating knowledge about single genes or pathways into a more comprehensive understanding of development.

Signal Destruction Tunes the Zone of Activation in Spatially Distributed Signaling Networks

Diverse microbial communities coordinate group behaviors through signal exchange, such as the exchange of acyl-homoserine lactones (AHLs) by Gram-negative bacteria. Cellular communication is prone to interference by neighboring microbes. One mechanism of interference is signal destruction through the production of an enzyme that cleaves the signaling molecule. Here we examine the ability of one such interference enzyme, AiiA, to modulate signal propagation in a spatially distributed system of bacteria. We have developed an experimental assay to measure signal transduction and implement a theoretical model of signaling dynamics to predict how the system responds to interference. We show that titration of an interfering strain into a signaling network tunes the spatial range of activation over the centimeter length scale, quantifying the robustness of the signaling network to signal destruction and demonstrating the ability to program systems-level responses of spatially heterogeneous cellular networks.

Development of Next Generation Synthetic Biology Tools for Use in Streptomyces venezuelae

Streptomyces have a rich history as producers of important natural products and this genus of bacteria has recently garnered attention for its potential applications in the broader context of synthetic biology. However, the dearth of genetic tools available to control and monitor protein production precludes rapid and predictable metabolic engineering that is possible in hosts such as Escherichia coli or Saccharomyces cerevisiae. In an effort to improve genetic tools for Streptomyces venezuelae, we developed a suite of standardized, orthogonal integration vectors and an improved method to monitor protein production in this host. These tools were applied to characterize heterologous promoters and various attB chromosomal integration sites. A final study leveraged the characterized toolset to demonstrate its use in producing the biofuel precursor bisabolene using a chromosomally integrated expression system. These tools advance S. venezuelae to be a practical host for future metabolic engineering efforts.

Engineering robust and tunable spatial structures with synthetic gene circuits

Controllable spatial patterning is a major goal for the engineering of biological systems. Recently, synthetic gene circuits have become promising tools to achieve the goal; however, they need to possess both functional robustness and tunability in order to facilitate future applications. Here we show that, by harnessing the dual signaling and antibiotic features of nisin, simple synthetic circuits can direct Lactococcus lactis populations to form programmed spatial band-pass structures that do not require fine-tuning and are robust against environmental and cellular context perturbations. Although robust, the patterns are highly tunable, with their band widths specified by the external nisin gradient and cellular nisin immunity. Additionally, the circuits can direct cells to consistently generate designed patterns, even when the gradient is driven by structured nisin-producing bacteria and the patterning cells are composed of multiple species. A mathematical model successfully reproduces all of the observed patterns. Furthermore, the circuits allow us to establish predictable structures of synthetic communities and controllable arrays of cellular stripes and spots in space. This study offers new synthetic biology tools to program spatial structures. It also demonstrates that a deep mining of natural functionalities of living systems is a valuable route to build circuit robustness and tunability.

Insightful directed evolution of Escherichia coli quorum sensing promoter region of the lsrACDBFG operon: a tool for synthetic biology systems and protein expression

Quorum sensing (QS) regulates many natural phenotypes (e.q. virulence, biofilm formation, antibiotic resistance), and its components, when incorporated into synthetic genetic circuits, enable user-directed phenotypes. We created a library of Escherichia coli lsr operon promoters using error-prone PCR (ePCR) and selected for promoters that provided E. coli with higher tetracycline resistance over the native promoter when placed upstream of the tet(C) gene. Among the fourteen clones identified, we found several mutations in the binding sites of QS repressor, LsrR. Using site-directed mutagenesis we restored all p-lsrR-box sites to the native sequence in order to maintain LsrR repression of the promoter, preserving the other mutations for analysis. Two promoter variants, EP01rec and EP14rec, were discovered exhibiting enhanced protein expression. In turn, these variants retained their ability to exhibit the LsrR-mediated QS switching activity. Their sequences suggest regulatory linkage between CytR (CRP repressor) and LsrR. These promoters improve upon the native system and exhibit advantages over synthetic QS promoters previously reported. Incorporation of these promoters will facilitate future applications of QS-regulation in synthetic biology and metabolic engineering.

Processing Oscillatory Signals by Incoherent Feedforward Loops

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose.

From circadian clocks to ultradian rhythms, oscillatory signals are found ubiquitously in nature. These oscillations are crucial in the regulation of cellular processes. While the fundamental design principles underlying the generation of these oscillations are extensively studied, the mechanisms for decoding these signals are underappreciated. With implications in both the basic understanding of how cells process temporal signals and the design of synthetic systems, we use quantitative modeling to probe one mechanism, the counting of pulses. We demonstrate the capability of an Incoherent Feedforward Loop motif for the differentiation between sustained and oscillatory input signals.

Integrative systems and synthetic biology of cell-matrix adhesion sites

The complexity of cell-matrix adhesion convolves its roles in the development and functioning of multicellular organisms and their evolutionary tinkering. Cell-matrix adhesion is mediated by sites along the plasma membrane that anchor the actin cytoskeleton to the matrix via a large number of proteins, collectively called the integrin adhesome. Fundamental challenges for understanding how cell-matrix adhesion sites assemble and function arise from their multi-functionality, rapid dynamics, large number of components and molecular diversity. Systems biology faces these challenges in its strive to understand how the integrin adhesome gives rise to functional adhesion sites. Synthetic biology enables engineering intracellular modules and circuits with properties of interest. In this review I discuss some of the fundamental questions in systems biology of cell-matrix adhesion and how synthetic biology can help addressing them.

Synthetic mixed-signal computation in living cells

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells.

Digital and analogue gene circuits each have distinct advantages in natural and engineered cells. Here, Rubens et al. engineer synthetic gene circuits that implement mixed-signal digital and analogue computations in living cells.

Systematic Design of a Quorum Sensing-Based Biosensor for Enhanced Detection of Metal Ion in Escherichia Coli

With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. The non-degradability of heavy metals may lead to accumulation in food chains and the resulting toxicity could cause damage in organisms. Hence, detection techniques have gradually received attention. In this study, a quorum sensing (QS)-based amplifier is introduced to improve the detection performance of metal ion biosensing. The design utilizes diffusible signal molecules, which freely pass through the cell membrane into the environment to communicate with others. Bacteria cooperate via the cell-cell communication process, thereby displaying synchronous behavior, even if only a minority of the cells detect the metal ion. In order to facilitate the design, the ability of the engineered biosensor to detect metal ion is described in a steady state model. The design can be constructed according to user-oriented specifications by selecting adequate components from corresponding libraries, with the help of a genetic algorithm (GA)-based design method. The experimental results validate enhanced efficiency and detection performance of the quorum sensing-based biosensor of metal ions.

Novel Utilization of Terminators in the Design of Biologically Adjustable Synthetic Filters

Terminators, which signal the end of transcription processes, are typically placed behind the last coding sequence of an operon to prevent interference between transcript units in most biologically synthetic systems. Here, we seek to extend the usability of terminators in genetic system design by using terminators as regulatory genetic parts. Terminators with different impacts on their upstream and downstream genes are characterized in detail via dynamic modeling to predict the behavior of the overall genetic system. Some nonlinear effects of terminators observed in our terminator measurements potentially facilitate regulation of gene expression. Through dynamic modeling in silico, we find that such genetic systems may behave like genetic filters. In agreement with the simulations, we successfully implement genetic high-pass and bandpass filters in vivo, demonstrating the potential of using terminators as regulatory parts. The genetic bandpass filter in this work is implemented through the interdependence between genetic parts, in which the termination efficiency of a terminator varies with the strength of the upstream promoter. This design strategy for a bandpass filter requires fewer base pairs than the conventional strategy of concatenating high-pass and low-pass filters. Our results show that this novel utilization of terminators as regulatory parts may provide a new perspective for efficient design of genetic circuits. We believe that further exploration of the complicated dynamics of terminators is important in the development of synthetic biology.

Cell-Cell Communication in Yeast Using Auxin Biosynthesis and Auxin Responsive CRISPR Transcription Factors

An engineering framework for synthetic multicellular systems requires a programmable means of cell-cell communication. Such a communication system would enable complex behaviors, such as pattern formation, division of labor in synthetic microbial communities, and improved modularity in synthetic circuits. However, it remains challenging to build synthetic cellular communication systems in eukaryotes due to a lack of molecular modules that are orthogonal to the host machinery, easy to reconfigure, and scalable. Here, we present a novel cell-to-cell communication system in Saccharomyces cerevisiae (yeast) based on CRISPR transcription factors and the plant hormone auxin that exhibits several of these features. Specifically, we engineered a sender strain of yeast that converts indole-3-acetamide (IAM) into auxin via the enzyme iaaH from Agrobacterium tumefaciens. To sense auxin and regulate transcription in a receiver strain, we engineered a reconfigurable library of auxin-degradable CRISPR transcription factors (ADCTFs). Auxin-induced degradation is achieved through fusion of an auxin-sensitive degron (from IAA corepressors) to the CRISPR TF and coexpression with an auxin F-box protein. Mirroring the tunability of auxin perception in plants, our family of ADCTFs exhibits a broad range of auxin sensitivities. We characterized the kinetics and steady-state behavior of the sender and receiver independently as well as in cocultures where both cell types were exposed to IAM. In the presence of IAM, auxin is produced by the sender cell and triggers deactivation of reporter expression in the receiver cell. The result is an orthogonal, rewireable, tunable, and, arguably, scalable cell-cell communication system for yeast and other eukaryotic cells.

2- and 3-dimensional synthetic large-scale de novo patterning by mammalian cells through phase separation

Synthetic biology provides an opportunity for the construction and exploration of alternative solutions to biological problems - solutions different from those chosen by natural life. To this end, synthetic biologists have built new sensory systems, cellular memories, and alternative genetic codes. There is a growing interest in applying synthetic approaches to multicellular systems, especially in relation to multicellular self-organization. Here we describe a synthetic biological system that confers large-scale de novo patterning activity on 2-D and 3-D populations of mammalian cells. Instead of using the reaction-diffusion mechanisms common in real embryos, our system uses cadherin-mediated phase separation, inspired by the known phenomenon of cadherin-based sorting. An engineered self-organizing, large-scale patterning system requiring no prior spatial cue may be a significant step towards the construction of self-assembling synthetic tissues.

Modular protein switches derived from antibody mimetic proteins

Protein switches have potential applications as biosensors and selective protein therapeutics. Protein switches built by fusion of proteins with the prerequisite input and output functions are currently developed using an ad hoc process. A modular switch platform in which existing switches could be readily adapted to respond to any ligand would be advantageous. We investigated the feasibility of a modular protein switch platform based on fusions of the enzyme TEM-1 β-lactamase (BLA) with two different antibody mimetic proteins: designed ankyrin repeat proteins (DARPins) and monobodies. We created libraries of random insertions of the gene encoding BLA into genes encoding a DARPin or a monobody designed to bind maltose-binding protein (MBP). From these libraries, we used a genetic selection system for β-lactamase activity to identify genes that conferred MBP-dependent ampicillin resistance to Escherichia coli. Some of these selected genes encoded switch proteins whose enzymatic activity increased up to 14-fold in the presence of MBP. We next introduced mutations into the antibody mimetic domain of these switches that were known to cause binding to different ligands. To different degrees, introduction of the mutations resulted in switches with the desired specificity, illustrating the potential modularity of these platforms.

Single Cell Analysis of a Bacterial Sender-Receiver System

Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from Aliivibrio fischeri on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as ‘bacterial sensors’ for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their ‘sending power’ is determined.

Environmental changes bridge evolutionary valleys

In the basic fitness landscape metaphor for molecular evolution, evolutionary pathways are presumed to follow uphill steps of increasing fitness. How evolution can cross fitness valleys is an open question. One possibility is that environmental changes alter the fitness landscape such that low-fitness sequences reside on a hill in alternate environments. We experimentally test this hypothesis on the antibiotic resistance gene TEM-15 β-lactamase by comparing four evolutionary strategies shaped by environmental changes. The strategy that included initial steps of selecting for low antibiotic resistance (negative selection) produced superior alleles compared with the other three strategies. We comprehensively examined possible evolutionary pathways leading to one such high-fitness allele and found that an initially deleterious mutation is key to the allele's evolutionary history. This mutation is an initial gateway to an otherwise relatively inaccessible area of sequence space and participates in higher-order, positive epistasis with a number of neutral to slightly beneficial mutations. The ability of negative selection and environmental changes to provide access to novel fitness peaks has important implications for natural evolutionary mechanisms and applied directed evolution.

Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications

The ability to engineer synthetic systems in the biochemical context is constantly being improved and has a profound societal impact. Linear system design is one of the most pervasive methods applied in control tasks, and its biochemical realization has been proposed by Oishi and Klavins and advanced further in recent years. However, several technical issues remain unsolved. Specifically, the design process is not fully automated from specification at the transfer function level, systems once designed often lack dynamic adaptivity to environmental changes, matching rate constants of reactions is not always possible, and implementation may be approximative and greatly deviate from the specifications. Building upon the work of Oishi and Klavins, this paper overcomes these issues by introducing a design flow that transforms a transfer-function specification of a linear system into a set of chemical reactions, whose input-output response precisely conforms to the specification. This system is implementable using the DNA strand displacement technique. The underlying configurability is embedded into primitive components and template modules, and thus the entire system is adaptive. Simulation of DNA strand displacement implementation confirmed the feasibility and superiority of the proposed synthesis flow.

Insertion of a xylanase in xylose binding protein results in a xylose-stimulated xylanase

BACKGROUND

Product inhibition can reduce catalytic performance of enzymes used for biofuel production. Different mechanisms can cause this inhibition and, in most cases, the use of classical enzymology approach is not sufficient to overcome this problem. Here we have used a semi-rational protein fusion strategy to create a product-stimulated enzyme.

RESULTS

A semi-rational protein fusion strategy was used to create a protein fusion library where the Bacillus subtilis GH11 xylanase A (XynA) was inserted at 144 surface positions of the Escherichia coli xylose binding protein (XBP). Two XynA insertions at XBP positions 209 ([209]XBP-Xyn-XBP) and 262 ([262]XBP-Xyn-XBP) showed a 20% increased xylanolytic activity in the presence of xylose, conditions where native XynA is inhibited. Random linkers of 1-4 Gly/Ala residues were inserted at the XynA N- and C-termini in the [209]XBP and [262]XBP, and the chimeras 2091A and 2621B were isolated, showing a twofold increased xylanolytic activity in the presence of xylose and k cat values of 200 and 240 s(-1) in the 2091A and 2621B, respectively, as compared to 70 s(-1) in the native XynA. The xylose affinity of the XBP was unchanged in the chimeras, showing that the ~3- to 3.5-fold stimulation of catalytic efficiency by xylose was the result of allosteric coupling between the XBP and XynA domains. Molecular dynamics simulations of the chimeras suggested conformation alterations in the XynA on xylose binding to the XBP resulted in exposure of the catalytic cavity and increased mobility of catalytic site residues as compared to the native XynA.

CONCLUSIONS

These results are the first report of engineered glycosyl hydrolase showing allosteric product stimulation and suggest that the strategy may be more widely employed to overcome enzyme product inhibition and to improve catalytic performance. Graphical abstractProtein fusion of a GH11 xylanase (in red) and a xylose binding protein (XBP, in blue) results in a xylanase-XBP chimera that presents allosteric activation of the xylanase activity by xylose (shown as a space-filled molecule bound to the xylanase-XBP chimera).

Artificial cell-cell communication as an emerging tool in synthetic biology applications

Cell-cell communication is a widespread phenomenon in nature, ranging from bacterial quorum sensing and fungal pheromone communication to cellular crosstalk in multicellular eukaryotes. These communication modes offer the possibility to control the behavior of an entire community by modifying the performance of individual cells in specific ways. Synthetic biology, i.e., the implementation of artificial functions within biological systems, is a promising approach towards the engineering of sophisticated, autonomous devices based on specifically functionalized cells. With the growing complexity of the functions performed by such systems, both the risk of circuit crosstalk and the metabolic burden resulting from the expression of numerous foreign genes are increasing. Therefore, systems based on a single type of cells are no longer feasible. Synthetic biology approaches with multiple subpopulations of specifically functionalized cells, wired by artificial cell-cell communication systems, provide an attractive and powerful alternative. Here we review recent applications of synthetic cell-cell communication systems with a specific focus on recent advances with fungal hosts.

Systematic Biological Filter Design with a Desired I/O Filtering Response Based on Promoter-RBS Libraries

In this study, robust biological filters with an external control to match a desired input/output (I/O) filtering response are engineered based on the well-characterized promoter-RBS libraries and a cascade gene circuit topology. In the field of synthetic biology, the biological filter system serves as a powerful detector or sensor to sense different molecular signals and produces a specific output response only if the concentration of the input molecular signal is higher or lower than a specified threshold. The proposed systematic design method of robust biological filters is summarized into three steps. Firstly, several well-characterized promoter-RBS libraries are established for biological filter design by identifying and collecting the quantitative and qualitative characteristics of their promoter-RBS components via nonlinear parameter estimation method. Then, the topology of synthetic biological filter is decomposed into three cascade gene regulatory modules, and an appropriate promoter-RBS library is selected for each module to achieve the desired I/O specification of a biological filter. Finally, based on the proposed systematic method, a robust externally tunable biological filter is engineered by searching the promoter-RBS component libraries and a control inducer concentration library to achieve the optimal reference match for the specified I/O filtering response.

Rational design of a fusion protein to exhibit disulfide-mediated logic gate behavior

Synthetic cellular logic gates are primarily built from gene circuits owing to their inherent modularity. Single proteins can also possess logic gate functions and offer the potential to be simpler, quicker, and less dependent on cellular resources than gene circuits. However, the design of protein logic gates that are modular and integrate with other cellular components is a considerable challenge. As a step toward addressing this challenge, we describe the design, construction, and characterization of AND, ORN, and YES logic gates built by introducing disulfide bonds into RG13, a fusion of maltose binding protein and TEM-1 β-lactamase for which maltose is an allosteric activator of enzyme activity. We rationally designed these disulfide bonds to manipulate RG13’s allosteric regulation mechanism such that the gating had maltose and reducing agents as input signals, and the gates could be toggled between different gating functions using redox agents, although some gates performed suboptimally.