Malarial Antibody Detection with an Engineered Yeast Agglutination Assay.

Malaria, a disease caused by the Plasmodium parasite carried by Anopheles mosquitoes, is commonly diagnosed by microscopy of peripheral blood smears and with rapid diagnostic tests. Both methods show limited detection of low parasitemia that may maintain transmission and hinder malaria elimination. We have developed a novel agglutination assay in which modified Saccharomyces cerevisiae cells act as antigen-displaying bead-like particles to capture malaria antibodies. The Epidermal Growth Factor-1 like domain (EGF1) of the Plasmodium falciparum merozoite surface protein-1 (PfMSP-119) was displayed on the yeast surface and shown to be capable of binding antimalaria antibodies. Mixed with a second yeast strain displaying the Z domain of Protein A from Staphylococcus aureus and allowed to settle in a round-bottomed well, the yeast produce a visually distinctive agglutination test result: a tight "button" at a low level of malarial antibodies, and a diffuse "sheet" when higher antibody levels are present. Positive agglutination results were observed in malaria-positive human serum to a serum dilution of 1:100 to 1:125. Since the yeast cells are inexpensive to produce, the test may be amenable to local production in regions seeking malaria surveillance information to guide their elimination programs.

E.coli Nissle increases transcription of flagella assembly and formate hydrogenlyase genes in response to colitis.

Escherichia coli Nissle (EcN), a probiotic bacterium, has been employed in treating inflammatory bowel disease, but the nature of its therapeutic effect is not fully understood. Intestinal inflammation alters the environment, exposing the microbial population to new stresses and eliciting transcriptional responses. We administered EcN to germ-free mice and then compared its transcriptional response between DSS-treated and untreated conditions using RNA-seq analysis to identify 187 differentially expressed genes (119 upregulated, 68 downregulated) and verifying a subset with qRT-PCR. The upregulated genes included many involved in flagella biosynthesis and motility, as well as several members of the formate hydrogenlyase complex. Despite prior evidence that these pathways are both transcriptionally regulated by nitric oxide, in vitro tests did not establish that nitric oxide exposure alone elicited the transcriptional response. The results provide new information on the transcriptional response of EcN to inflammation and establish a basis for further investigation of its anti-inflammatory activity.

Improving the design of an oxidative stress sensing biosensor in yeast.

Transcription factor (TF)-based biosensors have proven useful for increasing biomanufacturing yields, large-scale functional screening, and in environmental monitoring. Most yeast TF-based biosensors are built from natural promoters, resulting in large DNA parts retaining considerable homology to the host genome, which can complicate biological engineering efforts. There is a need to explore smaller, synthetic biosensors to expand the options for regulating gene expression in yeast. Here, we present a systematic approach to improving the design of an existing oxidative stress sensing biosensor in Saccharomyces cerevisiae based on the Yap1 transcription factor. Starting from a synthetic core promoter, we optimized the activity of a Yap1-dependent promoter through rational modification of a minimalist Yap1 upstream activating sequence. Our novel promoter achieves dynamic ranges of activation surpassing those of the previously engineered Yap1-dependent promoter, while reducing it to only 171 base pairs. We demonstrate that coupling the promoter to a positive-feedback-regulated TF further improves the biosensor by increasing its dynamic range of activation and reducing its limit of detection. We have illustrated the robustness and transferability of the biosensor by reproducing its activity in an unconventional probiotic yeast strain, Saccharomyces boulardii. Our findings can provide guidance in the general process of TF-based biosensor design.

Role of Dilution Rate and Nutrient Availability in the Formation of Microbial Biofilms.

We revisited the mathematical model of the chemostat and examined consequences of considerably decreasing the concentration of limiting nutrient in the inflow for the growth of both the planktonic and biofilm cells in the chemostat tank (fermenter). The model predicts a substantially lower steady-state biomass of planktonic cells in response to decreasing inflowing nutrient concentration. Contrarily, the steady-state concentration of nutrient inside the fermenter is expected to remain the same, as long as the inflowing concentration does not fall below its value. This allows the biofilm cells to grow at a rate regulated only by the exchange rate of the medium (dilution rate). We maintained a strain of Enterococcus faecalis in a chemostat of our own design with limiting nutrient in the inflow set near saturation constant at three dilution rates (0.09, 0.28, and 0.81 h-1). The highest dilution rate was near the critical rate calculated by the model. The one-day total biofilm buildup was 21× larger and its estimated growth rate 2.4× higher at highest dilution rate than at the lowest one. This increased biofilm formation with increased dilution rates is in agreement with previously published data on pure and mixed continuous flow cultures.

Programmable T7-based synthetic transcription factors.

Despite recent progress on synthetic transcription factor generation in eukaryotes, there remains a need for high-activity bacterial versions of these systems. In synthetic biology applications, it is useful for transcription factors to have two key features: they should be orthogonal (influencing only their own targets, with minimal off-target effects), and programmable (able to be directed to a wide range of user-specified transcriptional start sites). The RNA polymerase of the bacteriophage T7 has a number of appealing properties for synthetic biological designs: it can produce high transcription rates; it is a compact, single-subunit polymerase that has been functionally expressed in a variety of organisms; and its viral origin reduces the connection between its activity and that of its host's transcriptional machinery. We have created a system where a T7 RNA polymerase is recruited to transcriptional start sites by DNA binding proteins, either directly or bridged through protein-protein interactions, yielding a modular and programmable system for strong transcriptional activation of multiple orthogonal synthetic transcription factor variants in Escherichia coli. To our knowledge this is the first exogenous, programmable activator system in bacteria.

Design and Experimental Validation of Small Activating RNAs Targeting an Exogenous Promoter in Human Cells.

It is increasingly practical to co-opt many native cellular components into use as elements of synthetic biological systems. We present the design and experimental investigation of the first exogenous genetic construct to be successfully targeted by RNA activation, a phenomenon whereby small double-stranded RNAs increase gene expression from sequence-similar promoters by a mechanism thought to be related to that of RNA interference. Our selection of activating RNA candidates was informed by a custom-written computer program designed to choose target sites in the promoter of interest according to a set of empirical optimality criteria drawn from prior research. Activating RNA candidates were assessed for activity against two exogenously derived target promoters, with successful candidates being subjected to further rounds of validation as a precaution against potential off-target effects. A genetic platform was assembled that allowed activating RNA candidates to be simultaneously screened both for positive activity on the target reporter gene and for possible nonspecific effects on cell metabolism. Several candidate sequences were tested to appraise the utility of this platform, with the most successful achieving a moderate activation level with minimal off-target effects.

Design principles for the analysis and construction of robustly homeostatic biological networks.

Homeostatic biological systems resist external disturbances, allowing cells and organisms to maintain a constant internal state despite perturbations from their surroundings. Many biological regulatory networks are known to act homeostatically, with examples including thermal adaptation, osmoregulation, and chemotaxis. Understanding the network topologies (sets of regulatory interactions) and biological parameter regimes that can yield homeostasis in a biological system is of interest both for the study of natural biological system, and in the context of designing new biological control schemes for use in synthetic biology. Here, we examine the mathematical properties of a function that maps a biological system's inputs to its outputs, we have formulated a novel criterion (the "cofactor condition") that compactly describes the conditions for homeostasis. We further analyze the problem of robust homeostasis, wherein the system is required to maintain homeostatic behavior when its parameter values are slightly altered. We use the cofactor condition to examine previously reported examples of robust homeostasis, showing that it is a useful way to unify a number of seemingly different analyses into a single framework. Based on the observation that all previous robustly homeostatic examples fall into one of three classes, we propose a "strong cofactor condition" and use it to provide an algorithm for designing new robustly homeostatic biological networks, giving both their topologies and constraints on their parameter values. Applying the design algorithm to a three-node biological network, we construct several robustly homeostatic genetic networks, uncovering network topologies not previously identified as candidates for exhibiting robust homeostasis.

Automated inference procedure for the determination of cell growth parameters.

The growth rate and carrying capacity of a cell population are key to the characterization of the population's viability and to the quantification of its responses to perturbations such as drug treatments. Accurate estimation of these parameters necessitates careful analysis. Here, we present a rigorous mathematical approach for the robust analysis of cell count data, in which all the experimental stages of the cell counting process are investigated in detail with the machinery of Bayesian probability theory. We advance a flexible theoretical framework that permits accurate estimates of the growth parameters of cell populations and of the logical correlations between them. Moreover, our approach naturally produces an objective metric of avoidable experimental error, which may be tracked over time in a laboratory to detect instrumentation failures or lapses in protocol. We apply our method to the analysis of cell count data in the context of a logistic growth model by means of a user-friendly computer program that automates this analysis, and present some samples of its output. Finally, we note that a traditional least squares fit can provide misleading estimates of parameter values, because it ignores available information with regard to the way in which the data have actually been collected.

An Escherichia coli system for evolving improved light-controlled DNA-binding proteins.

Light-switchable proteins offer numerous opportunities as tools for manipulating biological systems with exceptional degrees of spatiotemporal control. Most designed light-switchable proteins currently in use have not been optimised using the randomisation and selection/screening approaches that are widely used in other areas of protein engineering. Here we report an approach for screening light-switchable DNA-binding proteins that relies on light-dependent repression of the transcription of a fluorescent reporter. We demonstrate that the method can be used to recover a known light-switchable DNA-binding protein from a random library.

Design and characterization of a dual-mode promoter with activation and repression capability for tuning gene expression in yeast.

Modularity in controlling gene expression artificially is becoming an essential aspect of synthetic biology. Artificial transcriptional control of gene expression is one of the most well-developed methods for the design of novel synthetic regulatory networks. Such networks are intended to help understand natural cellular phenomena and to enable new biotechnological applications. Promoter sequence manipulation with cis-regulatory elements is a key approach to control gene expression transcriptionally. Here, we have designed a promoter that can be both activated and repressed, as a contribution to the library of synthetic biological 'parts'. Starting with the minimal cytochrome C (minCYC) promoter in yeast, we incorporated five steroid hormone responsive elements (SHREs) and one lac operator site, respectively, upstream and downstream of the TATA box. This allows activation through the testosterone-responsive androgen receptor, and repression through the LacI repressor. Exposure to varying concentrations of testosterone (to vary activation) and IPTG (to vary repression) demonstrated the ability to tune the promoter's output curve over a wide range. By integrating activating and repressing signals, the promoter permits a useful form of signal integration, and we are optimistic that it will serve as a component in future regulatory networks, including feedback controllers.

Tuning response curves for synthetic biology.

Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system's dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components. We begin by reviewing a mathematical formulation that captures a broad class of biological response curves and use this to define a standard set of varieties of tuning: vertical shifting, horizontal scaling, and the like. We then survey the experimental literature, classifying the results into our defined categories, and organizing them by regulatory level: transcriptional, post-transcriptional, and post-translational.

Physical constraints on biological integral control design for homeostasis and sensory adaptation.

Synthetic biology includes an effort to use design-based approaches to create novel controllers, biological systems aimed at regulating the output of other biological processes. The design of such controllers can be guided by results from control theory, including the strategy of integral feedback control, which is central to regulation, sensory adaptation, and long-term robustness. Realization of integral control in a synthetic network is an attractive prospect, but the nature of biochemical networks can make the implementation of even basic control structures challenging. Here we present a study of the general challenges and important constraints that will arise in efforts to engineer biological integral feedback controllers or to analyze existing natural systems. Constraints arise from the need to identify target output values that the combined process-plus-controller system can reach, and to ensure that the controller implements a good approximation of integral feedback control. These constraints depend on mild assumptions about the shape of input-output relationships in the biological components, and thus will apply to a variety of biochemical systems. We summarize our results as a set of variable constraints intended to provide guidance for the design or analysis of a working biological integral feedback controller.

An active intracellular device to prevent lethal disease outcomes in virus-infected bacterial cells.

Synthetic biology includes an effort to logically control cellular behavior. One long-term goal is to implement medical interventions inside living cells, creating intracellular "disease fighters"; one may imagine a system that detects viral infection and responds to halt the spread of the virus. Here, we explore a system designed to display some of the qualitative features that such disease prevention systems should have, while not claiming that the system itself has any medical application. An intracellular disease prevention mechanism should: lie dormant in the absence of the disease state; detect the onset of a lethal disease pathway; respond to halt or mitigate the disease's effects; and be subject to external deactivation when required. We have created a device that displays these properties, in the highly simplified case of a bacterial viral disease. Our system detects the onset of the lytic phase of bacteriophage lambda in Escherichia coli, responds by preventing this lethal pathway from being followed, and is deactivated by a temperature shift. We have formulated a mathematical model of the engineered system, using parameters obtained from the literature and by local experimental measurement, and shown that the model captures the essential experimental behavior of the system in most parameter regimes.

Minimal genetic device with multiple tunable functions.

The ability to design artificial genetic devices with predictable functions is critical to the development of synthetic biology. Given the highly variable requirements of biological designs, the ability to tune the behavior of a genetic device is also of key importance; such tuning will allow devices to be matched with other components into larger systems, and to be shifted into the correct parameter regimes to elicit desired behaviors. Here, we have developed a minimal synthetic genetic system that acts as a multifunction, tunable biodevice in the bacterium Escherichia coli. First, it acts as a biochemical AND gate, sensing the extracellular small molecules isopropyl ß-D -1-thiogalactopyranoside and anhydrotetracycline as two input signals and expressing enhanced green fluorescent protein as an output signal. Next, the output signal of the AND gate can be amplified by the application of another extracellular chemical, arabinose. Further, the system can generate a wide range of chemically tunable single input-output response curves, without any genetic alteration of the circuit, by varying the concentrations of a set of extracellular small molecules. We have developed and parameterized a simple transfer function model for the system, and shown that the model successfully explains and predicts the quantitative relationships between input and output signals in the system.

Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network.

It has long been known to control theorists and engineers that integral feedback control leads to, and is necessary for, "perfect" adaptation to step input perturbations in most systems. Consequently, implementation of this robust control strategy in a synthetic gene network is an attractive prospect. However, the nature of genetic regulatory networks (density-dependent kinetics and molecular signals that easily reach saturation) implies that the design and construction of such a device is not straightforward. In this study, we propose a generic two-promoter genetic regulatory network for the purpose of exhibiting perfect adaptation; our treatment highlights the challenges inherent in the implementation of a genetic integral controller. We also present a numerical case study for a specific realization of this two-promoter network, "constructed" using commonly available parts from the bacterium Escherichia coli. We illustrate the possibility of optimizing this network's transient response via analogy to a linear, free-damped harmonic oscillator. Finally, we discuss extensions of this two-promoter network to a proportional-integral controller and to a three-promoter network capable of perfect adaptation under conditions where first-order protein removal effects would otherwise disrupt the adaptation.

Increasing the efficiency of bacterial transcription simulations: when to exclude the genome without loss of accuracy.

BACKGROUND: Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome. The key parameter is the on-rate of RNAP binding to the promoter (k_on), and we compare the total number of transcripts produced from a plasmid vector generated as a function of this rate constant, for two versions of our gene expression model, one incorporating the host cell genome and one excluding it. By sweeping parameters, we identify the k_on range for which the difference between the genome and no-genome models drops below 5%, over a wide range of doubling times, mRNA degradation rates, plasmid copy numbers, and gene lengths. RESULTS: We assess the effect of the simulating the presence of the genome over a four-dimensional parameter space, considering: 24 min

Effects of protein maturation on the noise in gene expression.

Fluorescent proteins are frequently used as reporters for gene expression in living cells, either by being expressed in tandem with a protein of interest or through the creation of fusion proteins. The data yielded by the fluorescence output are of considerable interest in efforts to formulate quantitative models of cellular behavior underway in fields such as systems biology and synthetic biology. An often neglected aspect of these proteins, however, is their maturation: Before a fluorescent protein can generate a fluorescent signal, it must mature through a series of steps (folding, cyclization, and oxidation) that may take from many minutes to over a day. The presence of these maturation steps creates a distinction between the observed gene expression profile and the actual profile. We examine this effect through a simplified gene expression model and conclude that fluorescent protein maturation can have significant effects on estimates of both the mean protein levels and the variability in gene expression. The model shows that in many regimes, the observed variability will be increased by the maturation process, but indicates the existence of regimes in which the observed variability will actually be less than the true variability of the target protein. The latter effect arises from a low-pass filtering effect introduced by the chain of maturation reactions. The results suggest that the maturation of fluorescent proteins should be taken into account when using such proteins as quantitative indicators of gene expression levels.

Plasmid-borne prokaryotic gene expression: sources of variability and quantitative system characterization.

One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs. We study two variants of the simplest such element, an unregulated promoter constitutively expressing its gene, against the varying genomic background of four Escherichia coli cell strains. Absolute protein numbers and rates of expression vary with both cell strain and plasmid type, as does the variability of expression across the population. Total variability is most strongly coupled to the cell division process, and after cell size is scaled away, plasmid copy number regulation emerges as a significant effect. We present simple models that capture the main features of the system behavior. Our results confirm that complex interactions between plasmids and their hosts can have significant effects on both expression and variability, even in deliberately simplified systems.

Dark proteins: effect of inclusion body formation on quantification of protein expression.

Plasmid-borne gene expression systems have found wide application in the emerging fields of systems biology and synthetic biology, where plasmids are used to implement simple network architectures, either to test systems biology hypotheses about issues such as gene expression noise or as a means of exerting artificial control over a cell's dynamics. In both these cases, fluorescent proteins are commonly applied as a means of monitoring the expression of genes in the living cell, and efforts have been made to quantify protein expression levels through fluorescence intensity calibration and by monitoring the partitioning of proteins among the two daughter cells after division; such quantification is important in formulating the predictive models desired in systems and synthetic biology research. A potential pitfall of using plasmid-based gene expression systems is that the high protein levels associated with expression from plasmids can lead to the formation of inclusion bodies, insoluble aggregates of misfolded, nonfunctional proteins that will not generate fluorescence output; proteins caught in these inclusion bodies are thus "dark" to fluorescence-based detection methods. If significant numbers of proteins are incorporated into inclusion bodies rather than becoming biologically active, quantitative results obtained by fluorescent measurements will be skewed; we investigate this phenomenon here. We have created two plasmid constructs with differing average copy numbers, both incorporating an unregulated promoter (P(LtetO-1) in the absence of TetR) expressing the GFP derivative enhanced green fluorescent protein (EGFP), and inserted them into Escherichia coli bacterial cells (a common model organism for work on the dynamics of prokaryotic gene expression). We extracted the inclusion bodies, denatured them, and refolded them to render them active, obtaining a measurement of the average number of EGFP per cell locked into these aggregates; at the same time, we used calibrated fluorescent intensity measurements to determine the average number of active EGFP present per cell. Both measurements were carried out as a function of cellular doubling time, over a range of 45-75 min. We found that the ratio of inclusion body EGFP to active EGFP varied strongly as a function of the cellular growth rate, and that the number of "dark" proteins in the aggregates could in fact be substantial, reaching ratios as high as approximately five proteins locked into inclusion bodies for every active protein (at the fastest growth rate), and dropping to ratios well below 1 (for the slowest growth rate). Our results suggest that efforts to compare computational models to protein numbers derived from fluorescence measurements should take inclusion body loss into account, especially when working with rapidly growing cells.

Simplification of stochastic chemical reaction models with fast and slow dynamics.

Biological systems often involve chemical reactions occurring in low-molecule-number regimes, where fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are generally quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements. While many model simplification approaches have been developed for deterministic systems, there has been limited work on applying these approaches to stochastic modelling. Here, we describe a method that reduces the complexity of stochastic biochemical network models, and apply this method to the reduction of a mammalian signalling cascade and a detailed model of the process of bacterial gene expression. Our results indicate that the simplified model gives an accurate representation for not only the average numbers of all species, but also for the associated fluctuations and statistical parameters.