Rewiring bacterial two-component systems by modular DNA-binding domain swapping

Engineering two-component systems for advanced biosensing: From architecture to applications in biotechnology

Two-component systems (TCSs) are prevalent signaling pathways in bacteria. These systems mediate phosphotransfer between histidine kinase and a response regulator, facilitating responses to diverse physical, chemical, and biological stimuli. Advancements in synthetic and structural biology have repurposed TCSs for applications in monitoring heavy metals, disease-associated biomarkers, and the production of bioproducts. However, the utility of many TCS biosensors is hindered by undesired performance due to the lack of effective engineering methods. Here, we briefly discuss the architectures and regulatory mechanisms of TCSs. We also summarize the recent advancements in TCS engineering by experimental or computational-based methods to fine-tune the biosensor functional parameters, such as response curve and specificity. Engineered TCSs have great potential in the medical, environmental, and biorefinery fields, demonstrating a crucial role in a wide area of biotechnology.

Rcs signal transduction system in Escherichia coli: Composition, related functions, regulatory mechanism, and applications

The regulator of capsule synthesis (Rcs) system, an atypical two-component system prevalent in numerous gram-negative bacteria, serves as a sophisticated regulatory phosphorylation cascade mechanism. It plays a pivotal role in perceiving environmental stress and regulating the expression of downstream genes to ensure host survival. During the signaling transduction process, various proteins participate in phosphorylation to further modulate signal inputs and outputs. Although the structure of core proteins related to the Rcs system has been partially well-defined, and two models have been proposed to elucidate the intricate molecular mechanisms underlying signal sensing, a systematic characterization of the signal transduction process of the Rcs system remains challenging. Furthermore, exploring its corresponding regulator outputs is also unremitting. This review aimed to shed light on the regulation of bacterial virulence by the Rcs system. Moreover, with the assistance of the Rcs system, biosynthesis technology has developed high-value target production. Additionally, via this review, we propose designing chimeric Rcs biosensor systems to expand their application as synthesis tools. Finally, unsolved challenges are highlighted to provide the basic direction for future development of the Rcs system.

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

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.

Data-driven learning of structure augments quantitative prediction of biological responses

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Searching for the optimal microbial factory: high-throughput biosensors and analytical techniques for screening small molecules

High-throughput screening technologies have been lacking in comparison to the plethora of high-throughput genetic diversification techniques developed in biotechnology. This review explores the challenges and advancements in high-throughput screening for high-value natural products, focusing on the critical need to expand ligand targets for biosensors and increase the throughput of analytical techniques in screening microbial cell libraries for optimal strain performance. The engineering techniques to broaden the scope of ligands for biosensors, such as transcription factors, G protein-coupled receptors and riboswitches are discussed. On the other hand, integration of microfluidics with traditional analytical methods is explored, covering fluorescence-activated cell sorting, Raman-activated cell sorting and mass spectrometry, emphasising recent developments in maximising throughput.

A domain swapping strategy to create modular transcriptional regulators for novel topology in genetic network

Transcriptional regulators generate connections between biological signals and genetic outputs. They are used robustly for sensing input signals in building genetic circuits. However, each regulator can only generate a fixed connection, which generates constraints in linking multiple signals for more complex processes. Recent studies discovered that a domain swapping strategy can be applied to various regulator families to create modular regulators for new signal-output connections, significantly broadening possibilities in circuit design. Here we review the development of this emerging strategy, the use of resulting modular regulators for creating novel genetic response behaviors, and current limitations and solutions for further advancing the design of modular regulators.

Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies

DNA-binding response regulators (DBRRs) are a broad class of proteins that operate in tandem with their partner kinase proteins to form two-component signal transduction systems in bacteria. Typical DBRRs are composed of two domains where the conserved N-terminal domain accepts transduced signals and the evolutionarily diverse C-terminal domain binds to DNA. These domains are assumed to be functionally independent, and hence recombination of the two domains should yield novel DBRRs of arbitrary input/output response, which can be used as biosensors. This idea has been proved to be successful in some cases; yet, the error rate is not trivial. Improvement of the success rate of this technique requires a deeper understanding of the linker-domain and inter-domain residue interactions, which have not yet been thoroughly examined. Here, we studied residue coevolution of DBRRs of the two main subfamilies (OmpR and NarL) using large collections of bacterial amino acid sequences to extensively investigate the evolutionary signatures of linker-domain and inter-domain residue interactions. Coevolutionary analysis uncovered evolutionarily selected linker-domain and inter-domain residue interactions of known experimental structures, as well as previously unknown inter-domain residue interactions. We examined the possibility of these inter-domain residue interactions as contacts that stabilize an inactive conformation of the DBRR where DNA binding is inhibited for both subfamilies. The newly gained insights on linker-domain/inter-domain residue interactions and shared inactivation mechanisms improve the understanding of the functional mechanism of DBRRs, providing clues to efficiently create functional DBRR-based biosensors. Additionally, we show the feasibility of applying coevolutionary landscape models to predict the functionality of domain-swapped DBRR proteins. The presented result demonstrates that sequence information can be used to filter out bioengineered DBRR proteins that are predicted to be nonfunctional due to a high negative predictive value.

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity (r2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon (r2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean).IMPORTANCEThe ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

REC protein family expansion by the emergence of a new signaling pathway

IMPORTANCE

We explore when and why large classes of proteins expand into new sequence space. We used an unsupervised machine learning approach to observe the sequence landscape of REC domains of bacterial response regulator proteins. We find that within-gene recombination can switch effector domains and, consequently, change the regulatory context of the duplicated protein.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

CpxA/R-Controlled Nitroreductase Expression as Target for Combinatorial Therapy against Uropathogens by Promoting Reactive Oxygen Species Generation

The antibiotic resistances emerged in uropathogens lead to accumulative treatment failure and recurrent episodes of urinary tract infection (UTI), necessitating more innovative therapeutics to curb UTI before systematic infection. In the current study, the combination of amikacin and nitrofurantoin is found to synergistically eradicate Gram-negative uropathogens in vitro and in vivo. The mechanistic analysis demonstrates that the amikacin, as an aminoglycoside, induced bacterial envelope stress by introducing mistranslated proteins, thereby constitutively activating the cpxA/R two-component system (Cpx signaling). The activation of Cpx signaling stimulates the expression of bacterial major nitroreductases (nfsA/nfsB) through soxS/marA regulons. As a result, the CpxA/R-dependent nitroreductases overexpression generates considerable quantity of lethal reactive intermediates via nitroreduction and promotes the prodrug activation of nitrofurantoin. As such, these actions together disrupt the bacterial cellular redox balance with excessively-produced reactive oxygen species (ROS) as "Domino effect", accelerating the clearance of uropathogens. Although aminoglycosides are used as proof-of-principle to elucidate the mechanism, the synergy between nitrofurantoin is generally applicable to other Cpx stimuli. To summarize, this study highlights the potential of aminoglycoside-nitrofurantoin combination to replenish the arsenal against recurrent Gram-negative uropathogens and shed light on the Cpx signaling-controlled nitroreductase as a potential target to manipulate the antibiotic susceptibility.

In Vivo Detection of Low Molecular Weight Platform Chemicals and Environmental Contaminants by Genetically Encoded Biosensors

Genetically encoded biosensor systems operating in living cells are versatile, cheap, and transferable tools for the detection and quantification of a broad range of small molecules. This review presents state-of-the-art biosensor designs and assemblies, featuring transcription factor-, riboswitch-, and enzyme-coupled devices, highly engineered fluorescent probes, and emerging two-component systems. Importantly, (bioinformatic-assisted) strategies to resolve contextual issues, which cause biosensors to miss performance criteria in vivo, are highlighted. The optimized biosensing circuits can be used to monitor chemicals of low molecular mass (<200 g mol^-1) and physicochemical properties that challenge conventional chromatographical methods with high sensitivity. Examples herein include but are not limited to formaldehyde, formate, and pyruvate as immediate products from (synthetic) pathways for the fixation of carbon dioxide (CO₂), industrially important derivatives like small- and medium-chain fatty acids and biofuels, as well as environmental toxins such as heavy metals or reactive oxygen and nitrogen species. Lastly, this review showcases biosensors capable of assessing the biosynthesis of platform chemicals from renewable resources, the enzymatic degradation of plastic waste, or the bioadsorption of highly toxic chemicals from the environment. These applications offer new biosensor-based manufacturing, recycling, and remediation strategies to tackle current and future environmental and socioeconomic challenges including the wastage of fossil fuels, the emission of greenhouse gases like CO₂, and the pollution imposed on ecosystems and human health.

Engineering transmembrane signal transduction in synthetic membranes using two-component systems

Cells use signal transduction across their membranes to sense and respond to a wide array of chemical and physical signals. Creating synthetic systems which can harness cellular signaling modalities promises to provide a powerful platform for biosensing and therapeutic applications. As a first step toward this goal, we investigated how bacterial two-component systems (TCSs) can be leveraged to enable transmembrane-signaling with synthetic membranes. Specifically, we demonstrate that a bacterial two-component nitrate-sensing system (NarX-NarL) can be reproduced outside of a cell using synthetic membranes and cell-free protein expression systems. We find that performance and sensitivity of the TCS can be tuned by altering the biophysical properties of the membrane in which the histidine kinase (NarX) is integrated. Through protein engineering efforts, we modify the sensing domain of NarX to generate sensors capable of detecting an array of ligands. Finally, we demonstrate that these systems can sense ligands in relevant sample environments. By leveraging membrane and protein design, this work helps reveal how transmembrane sensing can be recapitulated outside of the cell, adding to the arsenal of deployable cell-free systems primed for real world biosensing.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Structural features of sensory two component systems: a synthetic biology perspective

All living organisms include a set of signaling devices that confer the ability to dynamically perceive and adapt to the fluctuating environment. Two-component systems are part of this sensory machinery that regulates the execution of different genetic and/or biochemical programs in response to specific physical or chemical signals. In the last two decades, there has been tremendous progress in our molecular understanding on how signals are detected, the allosteric mechanisms that control intramolecular information transmission and the specificity determinants that guarantee correct wiring. All this information is starting to be exploited in the development of new synthetic networks. Connecting multiple molecular players, analogous to programming lines of code, can provide the resources to build new sophisticated biocomputing systems. The Synthetic Biology field is starting to revolutionize several scientific fields, such as biomedicine and agriculture, propelling the development of new solutions. Expanding the spectrum of available nanodevices in the toolbox is key to unleash its full potential. This review aims to discuss, from a structural perspective, how to take advantage of the vast array of sensor and effector protein modules involved in two-component systems for the construction of new synthetic circuits.

Synthetic biology promotes the capture of CO2 to produce fatty acid derivatives in microbial cell factories

Environmental problems such as greenhouse effect, the consumption of fossil energy, and the increase of human demand for energy are becoming more and more serious, which force researcher to turn their attention to the reduction of CO2 and the development of renewable energy. Unsafety, easy to lead to secondary environmental pollution, cost inefficiency, and other problems limit the development of conventional CO2 capture technology. In recent years, many microorganisms have attracted much attention to capture CO2 and synthesize valuable products directly. Fatty acid derivatives (e.g., fatty acid esters, fatty alcohols, and aliphatic hydrocarbons), which can be used as a kind of environmentally friendly and renewable biofuels, are sustainable substitutes for fossil energy. In this review, conventional CO2 capture techniques pathways, microbial CO2 concentration mechanisms and fixation pathways were introduced. Then, the metabolic pathway and progress of direct production of fatty acid derivatives from CO2 in microbial cell factories were discussed. The synthetic biology means used to design engineering microorganisms and optimize their metabolic pathways were depicted, with final discussion on the potential of optoelectronic-microbial integrated capture and production systems.

Development of Whole-Cell Biosensors for Screening of Peptidoglycan-Targeting Antibiotics in a Gram-Negative Bacterium

There is an urgent need to develop novel antibiotics since antibiotic resistance is an increasingly serious threat to global public health. Whole-cell biosensors are one of the promising strategies for new antibiotic discovery. The peptidoglycan (PG) of the bacterial cell wall is one of the most important targets for antibiotics. However, the biosensors for the detection of PG-targeting antibiotics in Gram-negative bacteria have not been developed, mainly because of the lack of the regulatory systems that sense and respond to PG stress. Recently, we identified a novel two-component signal transduction system (PghKR) that is responsible for sensing and responding to PG damage in the Gram-negative bacterium Shewanella oneidensis. Based on this system, we developed biosensors for the detection of PG-targeting antibiotics. Using ampicillin as an inducer for PG stress and the bacterial luciferase LuxCDABE as the reporter, we found that the PghKR biosensors are specific to antibiotics targeting PG synthesis, including β-lactams, vancomycin, and d-cycloserine. Deletion of genes encoding PG permease AmpG and β-lactamase BlaA improves the sensitivity of the biosensors substantially. The PghKR biosensor in the background of ΔblaA is also functional on agar plates, providing a simple method for screening bacteria that produce PG-targeting antibiotics. IMPORTANCE The growing problem of antibiotic resistance in Gram-negative bacteria urgently needs new strategies so that researchers can develop novel antibiotics. Microbial whole-cell biosensors are capable of sensing various stimuli with a quantifiable output and show tremendous potential for the discovery of novel antibiotics. As the Achilles' heel of bacteria, the synthesis of the peptidoglycan (PG) is targeted by many antibiotics. However, the regulatory systems that sense and respond to PG-targeting stress in Gram-negative bacteria are reported rarely, restricting the development of biosensors for the detection of PG-targeting antibiotics. In this study, we developed a highly sensitive and specific biosensor based on a novel two-component system in the Gram-negative bacterium Shewanella oneidensis that is responsible for the sensing and responding to PG stress. Our biosensors have great potential for discovering novel antibiotics and determining the mode of action of antibiotics.

Achieving spatially precise diagnosis and therapy in the mammalian gut using synthetic microbial gene circuits

The mammalian gut and its microbiome form a temporally dynamic and spatially heterogeneous environment. The inaccessibility of the gut and the spatially restricted nature of many gut diseases translate into difficulties in diagnosis and therapy for which novel tools are needed. Engineered bacterial whole-cell biosensors and therapeutics have shown early promise at addressing these challenges. Natural and engineered sensing systems can be repurposed in synthetic genetic circuits to detect spatially specific biomarkers during health and disease. Heat, light, and magnetic signals can also activate gene circuit function with externally directed spatial precision. The resulting engineered bacteria can report on conditions in situ within the complex gut environment or produce biotherapeutics that specifically target host or microbiome activity. Here, we review the current approaches to engineering spatial precision for in vivo bacterial diagnostics and therapeutics using synthetic circuits, and the challenges and opportunities this technology presents.

Genetic Circuit Design in Rhizobacteria

Genetically engineered plants hold enormous promise for tackling global food security and agricultural sustainability challenges. However, construction of plant-based genetic circuitry is constrained by a lack of well-characterized genetic parts and circuit design rules. In contrast, advances in bacterial synthetic biology have yielded a wealth of sensors, actuators, and other tools that can be used to build bacterial circuitry. As root-colonizing bacteria (rhizobacteria) exert substantial influence over plant health and growth, genetic circuit design in these microorganisms can be used to indirectly engineer plants and accelerate the design-build-test-learn cycle. Here, we outline genetic parts and best practices for designing rhizobacterial circuits, with an emphasis on sensors, actuators, and chassis species that can be used to monitor/control rhizosphere and plant processes.

Real-time detection of response regulator phosphorylation dynamics in live bacteria

Bacteria utilize two-component system (TCS) signal transduction pathways to sense and adapt to changing environments. In a typical TCS, a stimulus induces a sensor histidine kinase (SHK) to phosphorylate a response regulator (RR), which then dimerizes and activates a transcriptional response. Here, we demonstrate that oligomerization-dependent depolarization of excitation light by fused mNeonGreen fluorescent protein probes enables real-time monitoring of RR dimerization dynamics in live bacteria. Using inducible promoters to independently express SHKs and RRs, we detect RR dimerization within seconds of stimulus addition in several model pathways. We go on to combine experiments with mathematical modeling to reveal that TCS phosphosignaling accelerates with SHK expression but decelerates with RR expression and SHK phosphatase activity. We further observe pulsatile activation of the SHK NarX in response to addition and depletion of the extracellular electron acceptor nitrate when the corresponding TCS is expressed from both inducible systems and the native chromosomal operon. Finally, we combine our method with polarized light microscopy to enable single-cell measurements of RR dimerization under changing stimulus conditions. Direct in vivo characterization of RR oligomerization dynamics should enable insights into the regulation of bacterial physiology.

Engineering whole-cell microbial biosensors: Design principles and applications in monitoring and treatment of heavy metals and organic pollutants

Biosensors have been widely used as cost-effective, rapid, in situ, and real-time analytical tools for monitoring environments. The development of synthetic biology has enabled emergence of genetically engineered whole-cell microbial biosensors. This review updates the design and optimization principles for a diverse array of whole-cell biosensors based on transcription factors (TF) including activators or repressors derived from heavy metal resistance systems, alkanes, and aromatics metabolic pathways of bacteria. By designing genetic circuits, the whole-cell biosensors could be engineered to intelligently sense heavy metals (Hg²⁺, Zn²⁺, Pb²⁺, Au³⁺, Cd²⁺, As³⁺, Ni²⁺, Cu²⁺, and UO₂²⁺) or organic compounds (alcohols, alkanes, phenols, and benzenes) through one-component or two-component system-based TFs, transduce signals through genetic amplifiers, and response as various outputs such as cell fluorescence and bioelectricity for monitoring heavy metals and organic pollutants in real conditions, synthetic curli and surface metal-binding peptides for in situ bio-sorption of heavy metals. We further review strategies that have been implemented to optimize the selectivity and correlation between ligand concentration and output signal of the TF-based biosensors, so as to meet requirements of practical applications. The optimization strategies include protein engineering to change specificities, promoter engineering to improve sensitivities, and genetic circuit-based amplification to enhance dynamic ranges via designing transcriptional amplifiers, logic gates, and feedback loops. At last, we outlook future trends in developing novel forms of biosensors.

Engineering Light-Control in Biology

Unraveling the transformative power of optogenetics in biology requires sophisticated engineering for the creation and optimization of light-regulatable proteins. In addition, diverse strategies have been used for the tuning of these light-sensitive regulators. This review highlights different protein engineering and synthetic biology approaches, which might aid in the development and optimization of novel optogenetic proteins (Opto-proteins). Focusing on non-neuronal optogenetics, chromophore availability, general strategies for creating light-controllable functions, modification of the photosensitive domains and their fusion to effector domains, as well as tuning concepts for Opto-proteins are discussed. Thus, this review shall not serve as an encyclopedic summary of light-sensitive regulators but aims at discussing important aspects for the engineering of light-controllable proteins through selected examples.

Construction and Application of a High-Throughput In Vivo Screening Platform for the Evolution of Nitrile Metabolism-Related Enzymes Based on a Desensitized Repressive Biosensor

Transcription factor (TF)-based biosensors are expected to serve as powerful tools for the high-throughput screening of biocatalytic systems; however, most of them respond to ligands in a narrow concentration range, which limits their application. In this study, we constructed a heterogenous niacin biosensor using the repressive TF BsNadR and its target promoters from Bacillus subtilis. The fine-tunable output of the niacin biosensor was expanded to a wide range of niacin concentrations (0-50 mM) through desensitization engineering, which was suitable for the accurate identification of differences in enzyme activity. Structural mechanism analysis indicated that weakening the affinity of BsNadR with the ligand niacin and with DNA alters its regulatory properties. Based on the desensitized niacin biosensor, a high-throughput in vivo screening platform was developed for evolving nitrile metabolism-related enzymes. The evolved nitrilase, amidase, and nitrile hydratase with 6.6-, 2.1-, and 21.3-fold improvements in activity were achieved, respectively. In addition, these mutants also exhibited elevated activity toward other cognate substrates, indicating the broad applicability of the screening platform. This study not only provided a universal high-throughput screening platform for different nitrile metabolism-related enzymes but also demonstrated the advantages of repressive biosensors and the vital role of desensitization engineering of the TF in the development of high-throughput screening platforms for enzymes.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles

Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Engineering temporal dynamics in microbial communities

Microbial communities are a key part to tackling global challenges in human health, environmental conservation, and sustainable agriculture in the coming decade. Recent advances in synthetic biology to study and modify microbial communities have led to important insights into their physiology and ecology. Understanding how targeted changes to microbial communities result in reproducible alterations of the community's intrinsic fluctuations and function is important for mechanistic reconstruction of microbiomes. Studies of synthetic microbial consortia and comparative analysis of communities in normal and disrupted states have revealed ecological principles that can be leveraged to engineer communities towards desired functions. Tools enabling temporal modulation and sensing of the community dynamics offer precise spatiotemporal control of functions, help to dissect microbial interaction networks, and improve predictions of population temporal dynamics. Here we discuss recent advances to manipulate microbiome dynamics through control of specific strain engraftment and abundance, modulation of cell-cell signaling for tuning population dynamics, infiltration of new functions in the existing community with in situ engineering, and in silico modeling of microbial consortia to predict community function and ecology.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Red Light Optogenetics in Neuroscience

Optogenetics, a field concentrating on controlling cellular functions by means of light-activated proteins, has shown tremendous potential in neuroscience. It possesses superior spatiotemporal resolution compared to the surgical, electrical, and pharmacological methods traditionally used in studying brain function. A multitude of optogenetic tools for neuroscience have been created that, for example, enable the control of action potential generation via light-activated ion channels. Other optogenetic proteins have been used in the brain, for example, to control long-term potentiation or to ablate specific subtypes of neurons. In in vivo applications, however, the majority of optogenetic tools are operated with blue, green, or yellow light, which all have limited penetration in biological tissues compared to red light and especially infrared light. This difference is significant, especially considering the size of the rodent brain, a major research model in neuroscience. Our review will focus on the utilization of red light-operated optogenetic tools in neuroscience. We first outline the advantages of red light for in vivo studies. Then we provide a brief overview of the red light-activated optogenetic proteins and systems with a focus on new developments in the field. Finally, we will highlight different tools and applications, which further facilitate the use of red light optogenetics in neuroscience.

The Red Edge: Bilin-Binding Photoreceptors as Optogenetic Tools and Fluorescence Reporters

This review adds the bilin-binding phytochromes to the Chemical Reviews thematic issue "Optogenetics and Photopharmacology". The work is structured into two parts. We first outline the photochemistry of the covalently bound tetrapyrrole chromophore and summarize relevant spectroscopic, kinetic, biochemical, and physiological properties of the different families of phytochromes. Based on this knowledge, we then describe the engineering of phytochromes to further improve these chromoproteins as photoswitches and review their employment in an ever-growing number of different optogenetic applications. Most applications rely on the light-controlled complex formation between the plant photoreceptor PhyB and phytochrome-interacting factors (PIFs) or C-terminal light-regulated domains with enzymatic functions present in many bacterial and algal phytochromes. Phytochrome-based optogenetic tools are currently implemented in bacteria, yeast, plants, and animals to achieve light control of a wide range of biological activities. These cover the regulation of gene expression, protein transport into cell organelles, and the recruitment of phytochrome- or PIF-tagged proteins to membranes and other cellular compartments. This compilation illustrates the intrinsic advantages of phytochromes compared to other photoreceptor classes, e.g., their bidirectional dual-wavelength control enabling instant ON and OFF regulation. In particular, the long wavelength range of absorption and fluorescence within the "transparent window" makes phytochromes attractive for complex applications requiring deep tissue penetration or dual-wavelength control in combination with blue and UV light-sensing photoreceptors. In addition to the wide variability of applications employing natural and engineered phytochromes, we also discuss recent progress in the development of bilin-based fluorescent proteins.

Bacterial two-component systems as sensors for synthetic biology applications

Two-component systems (TCSs) are a ubiquitous family of signal transduction pathways that enable bacteria to sense and respond to diverse physical, chemical, and biological stimuli outside and inside the cell. Synthetic biologists have begun to repurpose TCSs for applications in optogenetics, materials science, gut microbiome engineering, and soil nutrient biosensing, among others. New engineering methods including genetic refactoring, DNA-binding domain swapping, detection threshold tuning, and phosphorylation cross-talk insulation are being used to increase the reliability of TCS sensor performance and tailor TCS signaling properties to the requirements of specific applications. There is now potential to combine these methods with large-scale gene synthesis and laboratory screening to discover the inputs sensed by many uncharacterized TCSs and develop a large new family of genetically-encoded sensors that respond to an unrivaled breadth of stimuli.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Optogenetics in bacteria - applications and opportunities

Optogenetics holds the promise of controlling biological processes with superb temporal and spatial resolution at minimal perturbation. Although many of the light-reactive proteins used in optogenetic systems are derived from prokaryotes, applications were largely limited to eukaryotes for a long time. In recent years, however, an increasing number of microbiologists use optogenetics as a powerful new tool to study and control key aspects of bacterial biology in a fast and often reversible manner. After a brief discussion of optogenetic principles, this review provides an overview of the rapidly growing number of optogenetic applications in bacteria, with a particular focus on studies venturing beyond transcriptional control. To guide future experiments, we highlight helpful tools, provide considerations for successful application of optogenetics in bacterial systems, and identify particular opportunities and challenges that arise when applying these approaches in bacteria.

Adaptation and compensation in a bacterial gene regulatory network evolving under antibiotic selection

Gene regulatory networks allow organisms to generate coordinated responses to environmental challenges. In bacteria, regulatory networks are re-wired and re-purposed during evolution, though the relationship between selection pressures and evolutionary change is poorly understood. In this study, we discover that the early evolutionary response of Escherichia coli to the antibiotic trimethoprim involves derepression of PhoPQ signaling, an Mg²⁺-sensitive two-component system, by inactivation of the MgrB feedback-regulatory protein. We report that derepression of PhoPQ confers trimethoprim-tolerance to E. coli by hitherto unrecognized transcriptional upregulation of dihydrofolate reductase (DHFR), target of trimethoprim. As a result, mutations in mgrB precede and facilitate the evolution of drug resistance. Using laboratory evolution, genome sequencing, and mutation re-construction, we show that populations of E. coli challenged with trimethoprim are faced with the evolutionary ‘choice’ of transitioning from tolerant to resistant by mutations in DHFR, or compensating for the fitness costs of PhoPQ derepression by inactivating the RpoS sigma factor, itself a PhoPQ-target. Outcomes at this evolutionary branch-point are determined by the strength of antibiotic selection, such that high pressures favor resistance, while low pressures favor cost compensation. Our results relate evolutionary changes in bacterial gene regulatory networks to strength of selection and provide mechanistic evidence to substantiate this link.

Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions

Genetic sensors with unique combinations of DNA recognition and allosteric response can be created by hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from distinct transcriptional repressors. This module swapping approach is limited by incompatibility between DBMs and LBMs from different proteins, due to the loss of critical module-module interactions after hybridization. We determine a design strategy for restoring key interactions between DBMs and LBMs by using a computational model informed by coevolutionary traits in the LacI family. This model predicts the influence of proposed mutations on protein structure and function, quantifying the feasibility of each mutation for rescuing hybrid repressors. We accurately predict which hybrid repressors can be rescued by mutating residues to reinstall relevant module-module interactions. Experimental results confirm that dynamic ranges of gene expression induction were improved significantly in these mutants. This approach enhances the molecular and mechanistic understanding of LacI family proteins, and advances the ability to design modular genetic parts.

A Biosensor for Detection of Indole Metabolites

Microbially produced indole metabolites serve as a diverse family of interspecies and interkingdom signaling molecules in the context of human health, crop production, and antibiotic resistance. We mined the protein database for sensors of indole metabolites and developed a biosensor for indole-3-aldehyde (I3A). Microbially produced I3A has been associated with reducing inflammation in diseases such as ulcerative colitis by stimulating the aryl hydrocarbon receptor pathway. We engineered an E. coli strain embedded with a single plasmid carrying a chimeric two-component system that detects I3A. Our I3A receptor characterization confirmed binding site residues that contribute to the sensor's I3A detection range of 0.1-10 μM. This new I3A biosensor opens the door to sensing indole metabolites produced at various host-microbe interfaces and provides new parts for synthetic biology applications.

Engineering Bacteria to Monitor the Bleeding of Animals Using Far-Red Fluorescence

Microorganisms living in animals can function as drug delivery systems or as detectors for some diseases. Here, we developed a biosensor constructed by the deletion of hemF and harboring ho1, chuA, and bdfp1.6 in Escherichia coli. HemF is an enzyme involved in heme synthesis in E. coli. ChuA and HO1 can transfer extracellular heme into cells and generate biliverdin (BV). BDFP1.6 can bind BV autocatalytically, and it emits a far-red fluorescence signal at 667 nm. Therefore, we named this biosensor as the far-red light for bleeding detector (FRLBD). Our results indicated that the FRLBD was highly efficient and specific for detecting heme or blood in vitro. Moreover, the FRLBD could be used to detect bleeding in the zebrafish induced by aspirin, and a convolutional neural network was an appropriate model to identify the fluorescence features in the images.

Mucosal acidosis elicits a unique molecular signature in epithelia and intestinal tissue mediated by GPR31-induced CREB phosphorylation

Metabolic changes associated with tissue inflammation result in significant extracellular acidosis (EA). Within mucosal tissues, intestinal epithelial cells (IEC) have evolved adaptive strategies to cope with EA through the up-regulation of SLC26A3 to promote pH homeostasis. We hypothesized that EA significantly alters IEC gene expression as an adaptive mechanism to counteract inflammation. Using an unbiased RNA sequencing approach, we defined the impact of EA on IEC gene expression to define molecular mechanisms by which IEC respond to EA. This approach identified a unique gene signature enriched in cyclic AMP response element-binding protein (CREB)-regulated gene targets. Utilizing loss- and gain-of-function approaches in cultured epithelia and murine colonoids, we demonstrate that EA elicits prominent CREB phosphorylation through cyclic AMP-independent mechanisms that requires elements of the mitogen-activated protein kinase signaling pathway. Further analysis revealed that EA signals through the G protein-coupled receptor GPR31 to promote induction of FosB, NR4A1, and DUSP1. These studies were extended to an in vivo murine model in conjunction with colonization of a pH reporter Escherichia coli strain that demonstrated significant mucosal acidification in the TNFΔARE model of murine ileitis. Herein, we observed a strong correlation between the expression of acidosis-associated genes with bacterial reporter sfGFP intensity in the distal ileum. Finally, the expression of this unique EA-associated gene signature was increased during active inflammation in patients with Crohn's disease but not in the patient control samples. These findings establish a mechanism for EA-induced signals during inflammation-associated acidosis in both murine and human ileitis.

Programmable protein circuit design

A fundamental challenge in synthetic biology is to create molecular circuits that can program complex cellular functions. Because proteins can bind, cleave, and chemically modify one another and interface directly and rapidly with endogenous pathways, they could extend the capabilities of synthetic circuits beyond what is possible with gene regulation alone. However, the very diversity that makes proteins so powerful also complicates efforts to harness them as well-controlled synthetic circuit components. Recent work has begun to address this challenge, focusing on principles such as orthogonality and composability that permit construction of diverse circuit-level functions from a limited set of engineered protein components. These approaches are now enabling the engineering of circuits that can sense, transmit, and process information; dynamically control cellular behaviors; and enable new therapeutic strategies, establishing a powerful paradigm for programming biology.

Synthetic Biological Approaches for Optogenetics and Tools for Transcriptional Light-Control in Bacteria

Light has become established as a tool not only to visualize and investigate but also to steer biological systems. This review starts by discussing the unique features that make light such an effective control input in biology. It then gives an overview of how light-control came to progress, starting with photoactivatable compounds and leading up to current genetic implementations using optogenetic approaches. The review then zooms in on optogenetics, focusing on photosensitive proteins, which form the basis for optogenetic engineering using synthetic biological approaches. As the regulation of transcription provides a highly versatile means for steering diverse biological functions, the focus of this review then shifts to transcriptional light regulators, which are presented in the biotechnologically highly relevant model organism Escherichia coli.

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.

Ultrasensitive molecular controllers for quasi-integral feedback

Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. While it is known that biological feedback loops reduce the impact of noise and help shape kinetic responses, many questions remain about how to design molecular integral controllers. Here, we propose a modular strategy to build molecular quasi-integral feedback controllers, which involves following two design principles. The first principle is to utilize an ultrasensitive response, which determines the gain of the controller and influences the steady-state error. The second is to use a tunable threshold of the ultrasensitive response, which determines the equilibrium point of the system. We describe a reaction network, named brink controller, that satisfies these conditions by combining molecular sequestration and an activation/deactivation cycle. With computational models, we examine potential biological implementations of brink controllers, and we illustrate different example applications.

A glucose meter interface for point-of-care gene circuit-based diagnostics

Recent advances in cell-free synthetic biology have given rise to gene circuit-based sensors with the potential to provide decentralized and low-cost molecular diagnostics. However, it remains a challenge to deliver this sensing capacity into the hands of users in a practical manner. Here, we leverage the glucose meter, one of the most widely available point-of-care sensing devices, to serve as a universal reader for these decentralized diagnostics. We describe a molecular translator that can convert the activation of conventional gene circuit-based sensors into a glucose output that can be read by off-the-shelf glucose meters. We show the development of new glucogenic reporter systems, multiplexed reporter outputs and detection of nucleic acid targets down to the low attomolar range. Using this glucose-meter interface, we demonstrate the detection of a small-molecule analyte; sample-to-result diagnostics for typhoid, paratyphoid A/B; and show the potential for pandemic response with nucleic acid sensors for SARS-CoV-2.

Getting synthetic biology circuit-based sensors into field applications is still a challenge. Here the authors combine a circuit sensor with a glucose meter for small analyte and nucleic acid detection.

Phytophthora Root Rot Modifies the Composition of the Avocado Rhizosphere Microbiome and Increases the Abundance of Opportunistic Fungal Pathogens

The structure and function of rhizosphere microbial communities are affected by the plant health status. In this study, we investigated the effect of root rot on the avocado rhizosphere microbiome, using 16S rDNA and ITS sequencing. Furthermore, we isolated potential fungal pathogens associated with root rot symptoms and assessed their pathogenic activity on avocado. We found that root rot did not affect species richness, diversity or community structure, but induced changes in the relative abundance of several microbial taxa. Root rot increased the proportion of Pseudomonadales and Burkholderiales in the rhizosphere but reduced that of Actinobacteria, Bacillus spp. and Rhizobiales. An increase in putative opportunistic fungal pathogens was also detected in the roots of symptomatic trees; the potential pathogenicity of Mortierella sp., Fusarium spp., Lasiodiplodia sp. and Scytalidium sp., is reported for the first time for the State of Veracruz, Mexico. Root rot also potentially modified the predicted functions carried out by rhizobacteria, reducing the proportion of categories linked with the lipid and amino-acid metabolisms whilst promoting those associated with quorum sensing, virulence, and antibiotic resistance. Altogether, our results could help identifying microbial taxa associated to the disease causal agents and direct the selection of plant growth-promoting bacteria for the development of biocontrol microbial consortia.

Characterizing Genetic Parts and Devices Using RNA Sequencing

Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.

A Split Methyl Halide Transferase AND Gate That Reports by Synthesizing an Indicator Gas

Monitoring microbial reactions in highly opaque or autofluorescent environments like soils, seawater, and wastewater remains challenging. To develop a simple approach for observing post-translational reactions within microbes situated in environmental matrices, we designed a methyl halide transferase (MHT) fragment complementation assay that reports by synthesizing an indicator gas. We show that backbone fission within regions of high sequence variability in the Rossmann domain yields split MHT (sMHT) AND gates whose fragments cooperatively associate to synthesize CH₃Br. Additionally, we identify a sMHT whose fragments require fusion to pairs of interacting partner proteins for maximal activity. We also show that sMHT fragments fused to FKBP12 and the FKBP-rapamycin binding domain of mTOR display significantly enhanced CH₃Br production in the presence of rapamycin. This gas production is reversed in the presence of the competitive inhibitor of FKBP12/FKPB dimerization, indicating that sMHT is a reversible reporter of post-translational reactions. This sMHT represents the first genetic AND gate that reports on protein-protein interactions via an indicator gas. Because indicator gases can be measured in the headspaces of complex environmental samples, this assay should be useful for monitoring the dynamics of diverse molecular interactions within microbes situated in hard-to-image marine and terrestrial matrices.