Genetic sensor for strong methylating compounds

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Translating New Synthetic Biology Advances for Biosensing Into the Earth and Environmental Sciences

The rapid diversification of synthetic biology tools holds promise in making some classically hard-to-solve environmental problems tractable. Here we review longstanding problems in the Earth and environmental sciences that could be addressed using engineered microbes as micron-scale sensors (biosensors). Biosensors can offer new perspectives on open questions, including understanding microbial behaviors in heterogeneous matrices like soils, sediments, and wastewater systems, tracking cryptic element cycling in the Earth system, and establishing the dynamics of microbe-microbe, microbe-plant, and microbe-material interactions. Before these new tools can reach their potential, however, a suite of biological parts and microbial chassis appropriate for environmental conditions must be developed by the synthetic biology community. This includes diversifying sensing modules to obtain information relevant to environmental questions, creating output signals that allow dynamic reporting from hard-to-image environmental materials, and tuning these sensors so that they reliably function long enough to be useful for environmental studies. Finally, ethical questions related to the use of synthetic biosensors in environmental applications are discussed.

Shining light on molecular communication

Molecules and combinations of molecules are the natural communication currency of microbes; microbes have evolved and been engineered to sense a variety of compounds, often with exquisite sensitivity. The availability of microbial biosensors, combined with the ability to genetically engineer biological circuits to process information, make microbes attractive bionanomachines for propagating information through molecular communication (MC) networks. However, MC networks built entirely of biological components suffer a number of limitations. They are extremely slow due to processing and propagation delays and must employ simple algorithms due to the still limited computational capabilities of biological circuits. In this work, we propose a hybrid bio-electronic framework which utilizes biological components for sensing but offloads processing and computation to traditional electronic systems and communication infrastructure. This is achieved by using tools from the burgeoning field of optogenetics to trigger biosensing through an optoelectronic interface, alleviating the need for computation and communication in the biological domain.

Engineering transcription factor-based biosensors for repressive regulation through transcriptional deactivation design in Saccharomyces cerevisiae

Background

With the development of engineering the microbial cell factories, biosensors have been used widely for regulation of cellular metabolism and high-throughput screening. However, most of the biosensors constructed in Saccharomyces cerevisiae are designed for transcriptional activation. Very few studies have dedicated to the development of genetic circuit for repressive regulation, which is also indispensable for the dynamic control of metabolism.

Results

In this study, through transcriptional deactivation design, we developed transcription-factor-based biosensors to allow repressive regulation in response to ligand. Using a malonyl-CoA sensing system as an example, the biosensor was constructed and systematically engineered to optimize the dynamic range by comparing transcriptional activity of the activators, evaluating the positions and numbers of the operators in the promoter and comparing the effects of different promoters. A biosensor with 82% repression ratio was obtained. Based on this design principle, another two biosensors, which sense acyl-CoA or xylose and downregulate gene expression, were also successfully constructed.

Conclusions

Our work systematically optimized the biosensors for repressive regulation in yeast for the first time. It provided useful framework to construct similar biosensors. Combining the widely reported biosensors for transcriptional activation with the biosensors developed here, it is now possible to construct biosensors with opposing transcriptional activities in yeast.

Biosensors design in yeast and applications in metabolic engineering

Engineering microbial cell factories is a potential approach of sustainable production of chemicals, fuels and pharmaceuticals. However, testing the production of molecules in high throughput is still a time-consuming and laborious process since product synthesis usually does not confer a clear phenotype. Therefore, it is necessary to develop new techniques for fast high-producer screening. Genetically encoded biosensors are considered to be promising devices for high-throughput analysis owing to their ability to sense metabolites and couple detection to an actuator, thereby facilitating the rapid detection of small molecules at single-cell level. Here, we review recent advances in the design and engineering of biosensors in Saccharomyces cerevisiae, and their applications in metabolic engineering. Three types of biosensor are introduced in this review: transcription factor based, RNA-based and enzyme-coupled biosensors. The studies to improve the features of biosensors are also described. Moreover, we summarized their metabolic engineering applications in dynamic regulation and high producer selection. Current challenges in biosensor design and future perspectives on sensor applications are also discussed.

Engineering metabolite-responsive transcriptional factors to sense small molecules in eukaryotes: current state and perspectives

Nature has evolved exquisite sensing mechanisms to detect cellular and environmental signals surrounding living organisms. These biosensors have been widely used to sense small molecules, detect environmental cues and diagnose disease markers. Metabolic engineers and synthetic biologists have been able to exploit metabolites-responsive transcriptional factors (MRTFs) as basic tools to rewire cell metabolism, reprogram cellular activity as well as boost cell’s productivity. This is commonly achieved by integrating sensor-actuator systems with biocatalytic functions and dynamically allocating cellular resources to drive carbon flux toward the target pathway. Up to date, most of identified MRTFs are derived from bacteria. As an endeavor to advance intelligent biomanufacturing in yeast cell factory, we will summarize the opportunities and challenges to transfer the bacteria-derived MRTFs to expand the small-molecule sensing capability in eukaryotic cells. We will discuss the design principles underlying MRTF-based biosensors in eukaryotic cells, including the choice of reliable reporters and the characterization tools to minimize background noise, strategies to tune the sensor dynamic range, sensitivity and specificity, as well as the criteria to engineer activator and repressor-based biosensors. Due to the physical separation of transcription and protein expression in eukaryotes, we argue that nuclear import/export mechanism of MRTFs across the nuclear membrane plays a critical role in regulating the MRTF sensor dynamics. Precisely-controlled MRTF response will allow us to repurpose the vast majority of transcriptional factors as molecular switches to achieve temporal or spatial gene expression in eukaryotes. Uncovering this knowledge will inform us fundamental design principles to deliver robust cell factories and enable the design of reprogrammable and predictable biological systems for intelligent biomanufacturing, smart therapeutics or precision medicine in the foreseeable future.

Synthetic Biology of Yeast

With the rapid development of DNA synthesis and next-generation sequencing, synthetic biology that aims to standardize, modularize, and innovate cellular functions, has achieved vast progress. Here we review key advances in synthetic biology of the yeast Saccharomyces cerevisiae, which serves as an important eukaryal model organism and widely applied cell factory. This covers the development of new building blocks, i.e., promoters, terminators and enzymes, pathway engineering, tools developments, and gene circuits utilization. We will also summarize impacts of synthetic biology on both basic and applied biology, and end with further directions for advancing synthetic biology in yeast.

Coupling of Human Rhodopsin to a Yeast Signaling Pathway Enables Characterization of Mutations Associated with Retinal Disease

G protein-coupled receptors (GPCRs) are crucial sensors of extracellular signals in eukaryotes, with multiple GPCR mutations linked to human diseases. With the growing number of sequenced human genomes, determining the pathogenicity of a mutation is challenging, but can be aided by a direct measurement of GPCR-mediated signaling. This is particularly difficult for the visual pigment rhodopsin-a GPCR activated by light-for which hundreds of mutations have been linked to inherited degenerative retinal diseases such as retinitis pigmentosa. In this study, we successfully engineered, for the first time, activation by human rhodopsin of the yeast mating pathway, resulting in signaling via a fluorescent reporter. We combine this novel assay for rhodopsin light-dependent activation with studies of subcellular localization, and the upregulation of the unfolded protein response in response to misfolded rhodopsin protein. We use these assays to characterize a panel of rhodopsin mutations with known molecular phenotypes, finding that rhodopsin maintains a similar molecular phenotype in yeast, with some interesting differences. Furthermore, we compare our assays in yeast with clinical phenotypes from patients with novel disease-linked mutations. We demonstrate that our engineered yeast strain can be useful in rhodopsin mutant classification, and in helping to determine the molecular mechanisms underlying their pathogenicity. This approach may also be applied to better understand the clinical relevance of other human GPCR mutations, furthering the use of yeast as a tool for investigating molecular mechanisms relevant to human disease.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Tracking yeast pheromone receptor Ste2 endocytosis using fluorogen-activating protein tagging

To observe internalization of the yeast pheromone receptor Ste2 by fluorescence microscopy in live cells in real time, we visualized only those molecules present at the cell surface at the time of agonist engagement (rather than the total cellular pool) by tagging this receptor at its N-terminus with an exocellular fluorogen-activating protein (FAP). A FAP is a single-chain antibody engineered to bind tightly a nonfluorescent, cell-impermeable dye (fluorogen), thereby generating a fluorescent complex. The utility of FAP tagging to study trafficking of integral membrane proteins in yeast, which possesses a cell wall, had not been examined previously. A diverse set of signal peptides and propeptide sequences were explored to maximize expression. Maintenance of the optimal FAP-Ste2 chimera intact required deletion of two, paralogous, glycosylphosphatidylinositol (GPI)-anchored extracellular aspartyl proteases (Yps1 and Mkc7). FAP-Ste2 exhibited a much brighter and distinct plasma membrane signal than Ste2-GFP or Ste2-mCherry yet behaved quite similarly. Using FAP-Ste2, new information was obtained about the mechanism of its internalization, including novel insights about the roles of the cargo-selective endocytic adaptors Ldb19/Art1, Rod1/Art4, and Rog3/Art7.

Lighting up yeast cell factories by transcription factor-based biosensors

Our ability to rewire cellular metabolism for the sustainable production of chemicals, fuels and therapeutics based on microbial cell factories has advanced rapidly during the last two decades. Especially the speed and precision by which microbial genomes can be engineered now allow for more advanced designs to be implemented and tested. However, compared to the methods developed for engineering cell factories, the methods developed for testing the performance of newly engineered cell factories in high throughput are lagging far behind, which consequently impacts the overall biomanufacturing process. For this purpose, there is a need to develop new techniques for screening and selection of best-performing cell factory designs in multiplex. Here we review the current status of the sourcing, design and engineering of biosensors derived from allosterically regulated transcription factors applied to the biotechnology work-horse budding yeast Saccharomyces cerevisiae. We conclude by providing a perspective on the most important challenges and opportunities lying ahead in order to harness the full potential of biosensor development for increasing both the throughput of cell factory development and robustness of overall bioprocesses.

Microbially derived biosensors for diagnosis, monitoring and epidemiology

Living cells have evolved to detect and process various signals and can self-replicate, presenting an attractive platform for engineering scalable and affordable biosensing devices. Microbes are perfect candidates: they are inexpensive and easy to manipulate and store. Recent advances in synthetic biology promise to streamline the engineering of microbial biosensors with unprecedented capabilities. Here we review the applications of microbially-derived biosensors with a focus on environmental monitoring and healthcare applications. We also identify critical challenges that need to be addressed in order to translate the potential of synthetic microbial biosensors into large-scale, real-world applications.

Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications

Within the Design-Build-Test Cycle for strain engineering, rapid product detection and selection strategies remain challenging and limit overall throughput. Here we summarize a wide variety of modalities that transduce chemical concentrations into easily measured absorbance, luminescence, and fluorescence signals. Specifically, we cover protein-based biosensors (including transcription factors), nucleic acid-based biosensors, coupled enzyme reactions, bioorthogonal chemistry, and fluorescent and chromogenic dyes and substrates as modalities for detection. We focus on the use of these methods for strain engineering and enzyme discovery and conclude with remarks on the current and future state of biosensor development for application in the metabolic engineering field.

The Risk of the Preparation of Artificial DNAs via an Interrupted Automated Solid-Phase Triester Method

In this paper, the risk of the preparation of artificial DNAs via an interrupted automated solid-phase triester procedure is discussed. These results differ completely from the corresponding results obtained by a gradual non-automated synthesis, indicated as solution-phase chemistry. With experimental results focused on model systems under various conditions and molecular-mechanics calculations, it feels as a challenge to offer an explanation for this different behavior. Attention is briefly given to click-linked DNA fragments as a substitute of phosphate linkages.

Synthetic biosensors for precise gene control and real-time monitoring of metabolites

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities

Ligand-responsive transcription factors in prokaryotes found simple small molecule-inducible gene expression systems. These have been extensively used for regulated protein production and associated biosynthesis of fine chemicals. However, the promoter and protein engineering approaches traditionally used often pose significant restrictions to predictably and rapidly tune the expression profiles of inducible expression systems. Here, we present a new unified and rational tuning method to amplify the sensitivity and dynamic ranges of versatile small molecule-inducible expression systems. We employ a systematic variation of the concentration of intracellular receptors for transcriptional control. We show that a low density of the repressor receptor (e.g. TetR and ArsR) in the cell can significantly increase the sensitivity and dynamic range, whereas a high activator receptor (e.g. LuxR) density achieves the same outcome. The intracellular concentration of receptors can be tuned in both discrete and continuous modes by adjusting the strength of their cognate driving promoters. We exemplified this approach in several synthetic receptor-mediated sensing circuits, including a tunable cell-based arsenic sensor. The approach offers a new paradigm to predictably tune and amplify ligand-responsive gene expression with potential applications in synthetic biology and industrial biotechnology.

Synthetic biology expands chemical control of microorganisms

The tools of synthetic biology allow researchers to change the ways engineered organisms respond to chemical stimuli. Decades of basic biology research and new efforts in computational protein and RNA design have led to the development of small molecule sensors that can be used to alter organism function. These new functions leap beyond the natural propensities of the engineered organisms. They can range from simple fluorescence or growth reporting to pathogen killing, and can involve metabolic coordination among multiple cells or organisms. Herein, we discuss how synthetic biology alters microorganisms' responses to chemical stimuli resulting in the development of microbes as toxicity sensors, disease treatments, and chemical factories.

Systems Biology and Synthetic Biology: A New Epoch for Toxicology Research

Systems biology and synthetic biology are emerging disciplines which are becoming increasingly utilised in several areas of bioscience. Toxicology is beginning to benefit from systems biology and we suggest in the future that is will also benefit from synthetic biology. Thus, a new era is on the horizon. This review illustrates how a suite of innovative techniques and tools can be applied to understanding complex health and toxicology issues. We review limitations confronted by the traditional computational approaches to toxicology and epidemiology research, using polycyclic aromatic hydrocarbons (PAHs) and their effects on adverse birth outcomes as an illustrative example. We introduce how systems toxicology (and their subdisciplines, genomic, proteomic, and metabolomic toxicology) will help to overcome such limitations. In particular, we discuss the advantages and disadvantages of mathematical frameworks that computationally represent biological systems. Finally, we discuss the nascent discipline of synthetic biology and highlight relevant toxicological centred applications of this technique, including improvements in personalised medicine. We conclude this review by presenting a number of opportunities and challenges that could shape the future of these rapidly evolving disciplines.