Predictive and interpretive simulation of green fluorescent protein expression in reporter bacteria

Biosensing of arsenic by whole-cell bacterial bioreporter immobilized on polycaprolactone (PCL) electrospun fiber

In recent years, heavy metals derived from several anthropogenic sources have both direct and indirect detrimental effects on the health of the environment and living organisms. Whole-cell bioreporters (WCBs) that can be used to monitor the levels of heavy metals in drinking and natural spring waters are important. In this study, whole-cell arsenic bacterial bioreporters were immobilized using polycaprolactone (PCL) electrospun fibers as the support material. The aim is to determine the properties of this immobilized bioreporter system by evaluating its performance in arsenic detection. Within the scope of the study, different growth media and fiber immobilization times were tested to determine the parameters affecting the fluorescent signals emitted by the immobilized bioreporter system in the presence of two dominant forms of arsenic, namely arsenite (As(III)) and arsenate (As(V)). In addition, the sensitivity, selectivity, response time, and shelf-life of the developed bioreporter system were evaluated. As far as the literature is concerned, this is the first study to investigate the potential of using PCL-electrospun fiber-immobilized fluorescent bacterial bioreporter for arsenic detection. This study will open new avenues in environmental arsenic monitoring.

Single-cell level LasR-mediated quorum sensing response of Pseudomonas aeruginosa to pulses of signal molecules

Quorum sensing (QS) is a communication form between bacteria via small signal molecules that enables global gene regulation as a function of cell density. We applied a microfluidic mother machine to study the kinetics of the QS response of Pseudomonas aeruginosa bacteria to additions and withdrawals of signal molecules. We traced the fast buildup and the subsequent considerably slower decay of a population-level and single-cell-level QS response. We applied a mathematical model to explain the results quantitatively. We found significant heterogeneity in QS on the single-cell level, which may result from variations in quorum-controlled gene expression and protein degradation. Heterogeneity correlates with cell lineage history, too. We used single-cell data to define and quantitatively characterize the population-level quorum state. We found that the population-level QS response is well-defined. The buildup of the quorum is fast upon signal molecule addition. At the same time, its decay is much slower following signal withdrawal, and the quorum may be maintained for several hours in the absence of the signal. Furthermore, the quorum sensing response of the population was largely repeatable in subsequent pulses of signal molecules.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

RNA fluorescence in situ hybridization (FISH) as a method to visualize bacterial colonization in the C. elegans gut

Caenorhabditis elegans is an excellent model to study host-microbe interactions as it is easy to visualize bacterial presence in their intestine. However, previous studies have shown that utilizing transgenic, fluorescent protein-expressing bacteria is not a reliable method to distinguish living bacteria from dead bacteria in the lumen of C. elegans . In this study, we compared methods for visualizing bacterial presence within the C. elegans intestine and found that RNA f luorescent i n s itu h ybridization (RNA FISH) could distinguish the difference between intact and dead bacteria. Thus, we propose RNA FISH as the preferred method to visualize live bacterial presence in the intestines of C. elegans prior to fixation.

Measuring differential fitness costs and interactions between genetic cassettes using fluorescent spectrophotometry

In this article, we present a method for designing, executing, and analyzing data from a microbial competition experiment. We use fluorescent reporters to label different competing strains and resolve individual growth curves using a fluorescent spectrophotometer. Our comprehensive data analysis pipeline integrates multiple experiments to simultaneously infer sources of variation, extract selection coefficients, and estimate the genetic contributions to fitness for various synthetic genetic cassettes (SGCs). To demonstrate the method, we employ a synthetic biological system based on Escherichia coli. Strains carry 1 of 10 different plasmids and one of three genomically integrated fluorescent markers. All strains are co-cultured to obtain real-time measurements of optical density (total population density) and fluorescence (sub-population densities). We identify challenges in calibrating between fluorescence and density and of fluorescent proteins maturing at different rates. To resolve these issues, we compare two methods of fluorescence calibration and correct for maturation by measuring in vivo maturation times. We provide evidence of genetic interactions occurring between our SGCs and further show how to use our statistical model to test some hypotheses about microbial growth and the costs of protein expression.IMPORTANCEFluorescently labeled co-cultures are becoming increasingly popular. The approach proposed here offers a high standard for experimental design and data analysis to measure selection coefficients and growth rates in competition. Measuring competitive differences is useful in many laboratory studies, allowing for fitness cost-correction of growth rates and ecological interactions and testing hypotheses in synthetic biology. Using time-resolved growth curves, rather than endpoint measurements, for competition assays allows us to construct a detailed scientific model that can be used to ask questions about fine-grained phenomena, such as bacterial growth dynamics, as well as higher-level phenomena, such as the interactions between synthetic cassette expression.

Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria

The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, implement combinatorial regulation, and reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.

Dissecting the molecular dance: c-di-GMP, cAMP-CRP, and VfmH collaboration in pectate lyase regulation for Dickeya dadantii-unveiling the soft rot pathogen's strategy

IMPORTANCE

Bacteria respond to environmental changes and adapt to host systems. The response regulator VfmH of the Vfm quorum sensing system regulates a crucial virulence factor, pectate lyase (Pel), in Dickeya dadantii. At high c-di-GMP concentrations, VfmH binds c-di-GMP, resulting in the loss of its activation property in the Pel and virulence regulation in D. dadantii. VfmH binds to c-di-GMP via three conserved arginine residues, and mutations of these residues eliminate the c-di-GMP-related phenotypes of VfmH in Pel synthesis. Our data also show that VfmH interacts with CRP to regulate pelD transcription, thus integrating cyclic AMP and c-di-GMP signaling pathways to control virulence in D. dadantii. We propose that VfmH is an important intermediate factor incorporating quorum sensing and nucleotide signaling pathways for the collective regulation of D. dadantii pathogenesis.

Cyanobacterial bioreporter of nitrate bioavailability in aquatic ecosystems

The water eutrophication, resulting from the discharge of industrial and agricultural wastewater, leads to ecological degradation. However, to date, how to assess and manage the risks of water pollution, especially nitrogen pollution, remains a particularly noteworthy issue. Nitrate, the most important nitrogen compound, has become a bottleneck restricting total nitrogen management. The development of bioreporters monitoring nitrate pollution contributes to the estimation of water quality, especially the availability of nutrients. In this study, we obtained 9 bioreporters from 40 cyanobacterial derivatives which were constructed based on different hosts, copy numbers, and sensing elements and evaluated the performance of bioreporters. The results showed that single-celled Synechocystis was more sensitive to nitrate than filamentous Anabaena, that the reporter gene luxABCDE responded faster than sfgfp in most bioreporters, and that relatively medium-copy plasmid improved the performance of sensing elements. Nine bioreporters performed well in bioavailable nitrate detection, of which AD-AS-X and AR-NI-X, activated by nitrate repletion, had the shortest response time (2 h) and the widest response range (20-800 μM), respectively. Moreover, SR-GLN-SG, activated by nitrate deficiency, exhibited the best linear response (R2 = 0.998). After parameter optimization, exponential growth phase bioreporters, culture temperature of 30 °C, sample volume of 200 μL were determined as optimal monitoring conditions. We found that common water contaminants (copper, cadmium, and phosphorus) had no impact on the performance of bioreporters, indicating the stability of bioreporters. Six out of 9 bioreporters, especially the SR-NB-X, were highly effective in detecting the bioavailable nitrate in wastewater sample. This study provides valuable references for developing more cyanobacterial bioreporters and their practical application in nitrate detection.

Erwinia amylovora Type III Secretion System Inhibitors Reduce Fire Blight Infection Under Field Conditions

Fire blight, caused by Erwinia amylovora, is an economically important disease in apples and pears worldwide. This pathogen relies on the type III secretion system (T3SS) to cause disease. Compounds that inhibit the function of the T3SS (T3SS inhibitors) have emerged as alternative strategies for bacterial plant disease management, as they block bacterial virulence without affecting growth, unlike traditional antibiotics. In this study, we investigated the mode of action of a T3SS inhibitor named TS108, a plant phenolic acid derivative, in E. amylovora. We showed that adding TS108 to an in vitro culture of E. amylovora repressed the expression of several T3SS regulon genes, including the master regulator gene hrpL. Further studies demonstrated that TS108 negatively regulates CsrB, a global regulatory small RNA, at the posttranscriptional level, resulting in a repression of hrpS, which encodes a key activator of hrpL. Additionally, TS108 has no impact on the expression of T3SS in Dickeya dadantii or Pseudomonas aeruginosa, suggesting that its inhibition of the E. amylovora T3SS is likely species specific. To better evaluate the performance of T3SS inhibitors in fire blight management, we conducted five independent field experiments in four states (Michigan, New York, Oregon, and Connecticut) from 2015 to 2022 and observed reductions in blossom blight incidence as high as 96.7% compared with untreated trees. In summary, the T3SS inhibitors exhibited good efficacy against fire blight.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Maturation models of fluorescent proteins are necessary for unbiased estimates of promoter activity

Fluorescent proteins (FPs) are a powerful tool to quantitatively monitor gene expression. The dynamics of a promoter and its regulation can be inferred from fluorescence data. The interpretation of fluorescent data, however, is strongly dependent on the maturation of FPs since different proteins mature in distinct ways. We propose a novel approach for analyzing fluorescent reporter data by incorporating maturation dynamics in the reconstruction of promoter activities. Our approach consists of developing and calibrating mechanistic maturation models for distinct FPs. These models are then used alongside a Bayesian approach to estimate promoter activities from fluorescence data. We demonstrate by means of targeted experiments in Escherichia coli that our approach provides robust estimates and that accounting for maturation is, in many cases, essential for the interpretation of gene expression data.

The phytopathogen Dickeya dadantii 3937 cpxR locus gene participates in the regulation of virulence and the global c-di-GMP network

Bacteria use signal transduction systems to sense and respond to their external environment. The two-component system CpxA/CpxR senses misfolded envelope protein stress and responds by up-regulating envelope protein factors and down-regulating virulence factors in several animal pathogens. Dickeya dadantii is a phytopathogen equipped with a type III secretion system (T3SS) for manipulating the host immune response. We found that deletion of cpxR enhanced the expression of the T3SS marker gene hrpA in a designated T3SS-inducing minimal medium (MM). In the ∆cpxR mutant, multiple T3SS and c-di-GMP regulators were also up-regulated. Subsequent analysis revealed that deletion of the phosphodiesterase gene egcpB in ∆cpxR abolished the enhanced T3SS expression. This suggested that CpxR suppresses EGcpB levels, causing low T3SS expression in MM. Furthermore, we found that the ∆cpxR mutant displayed low c-di-GMP phenotypes in biofilm formation and swimming. Increased production of cellular c-di-GMP by in trans expression of the diguanylate cyclase gene gcpA was negated in the ∆cpxR mutant. Here, we propose that CpxA/CpxR regulates T3SS expression by manipulating the c-di-GMP network, in turn modifying the multiple physiological activities involved in the response to environmental stresses in D. dadantii.

Methods to monitor bacterial growth and replicative rates at the single-cell level

The heterogeneity of bacterial growth and replicative rates within a population was proposed a century ago notably to explain the presence of bacterial persisters. The term "growth rate" at the single-cell level corresponds to the increase in size or mass of an individual bacterium while the "replicative rate" refers to its division capacity within a defined temporality. After a decades long hiatus, recent technical innovative approaches allow population growth and replicative rates heterogeneity monitoring at the single-cell level resuming in earnest. Among these techniques, the oldest and widely used is time-lapse microscopy, most recently combined with microfluidics. We also discuss recent fluorescence dilution methods informing only on replicative rates and best suited. Some new elegant single cell methods so far only sporadically used such as buoyant mass measurement and stable isotope probing have emerged. Overall, such tools are widely used to investigate and compare the growth and replicative rates of bacteria displaying drug-persistent behaviors to that of bacteria growing in specific ecological niches or collected from patients. In this review, we describe the current methods available, discussing both the type of queries these have been used to answer and the specific strengths and limitations of each method.

Transcription-Associated Fluorescence-Activated Droplet Sorting for Di-rhamnolipid Hyperproducers

Rhamnolipids (RLs) are biosurfactants with great economic significance that have been used extensively in multiple industries. Pseudomonas aeruginosa is a promising microorganism for sustainable RL production. However, current CTAB-MB based screening of RL-producing strains is time-consuming, labor-intensive, and unable to distinguish mono- and di-RL. In this study, we developed a novel transcription-associated fluorescence-activated droplet sorting (FADS) method to specifically target the di-RL hyperproducers. We first investigated critical factors associated with this method, including the specificity and sensitivity for discriminating di-RL overproducers from other communities. Validation of genotype-phenotype linkage between the GFP intensity, rhlC transcription, and di-RL production showed that rhlC transcription is closely correlated with di-RL production, and the GFP intensity is responsive to rhlC transcription, respectively. Using this platform, we screened out ten higher di-RL producing microorganisms, which produced 54-208% more di-RL than the model P. aeruginosa PAO1. In summary, the droplet-based microfluidic platform not only facilitates a more specific, reliable, and rapid screening of P. aeruginosa colonies with desired phenotypes, but also shows that intracellular transcription-associated GFP intensity can be used to measure the yield of di-RL between populations of droplets containing different environmental colonies. This method also can be integrated with transposon mutation libraries to target P. aeruginosa mutants.

Quorum-Sensing Master Regulator VfmE Is a c-di-GMP Effector That Controls Pectate Lyase Production in the Phytopathogen Dickeya dadantii

Dickeya dadantii is a phytopathogenic bacterium that causes diseases on a wide range of host plants. The pathogen secretes pectate lyases (Pel) through the type II secretion system (T2SS) that degrades the cell wall in host plants. The virulence of D. dadantii is controlled by the second messenger cyclic diguanylate monophosphate (c-di-GMP), and the homeostasis of c-di-GMP is maintained by a number of diguanylate cyclases and phosphodiesterases. Deletion of a phosphodiesterase ecpC repressed pelD transcription, and such repression can be suppressed by an additional deletion in vfmE. VfmE is an AraC type of transcriptional regulator in the Vfm quorum-sensing system. Our results suggest that VfmE is a c-di-GMP effector that functions as an activator of pel at low c-di-GMP concentrations and a repressor of pel at high c-di-GMP concentrations through regulation of the transcriptional activator SlyA. Multiple sequence alignment with known c-di-GMP effectors identified an RWIWR motif in VfmE that we demonstrate is required for the c-di-GMP binding. Mutation of R93D in the RxxxR motif eliminates the c-di-GMP-related phenotypes in Pel activity. Our results show that VfmE is not only a quorum-sensing regulator but also a c-di-GMP effector, suggesting that D. dadantii integrates the c-di-GMP signaling network with the Vfm quorum-sensing pathway during environmental adaptation. IMPORTANCE How bacteria integrate environmental cues from multiple sources to appropriately regulate adaptive phenotypes is a central question in microbiology. In Dickeya dadantii, the quorum-sensing regulator VfmE controls the key virulence factor pectate lyase (Pel). Here, we demonstrate that VfmE also binds to c-di-GMP, resulting in VfmE functioning as an activator of pel at low c-di-GMP concentrations and repressor of pel at high c-di-GMP concentrations. The RWIWR motif in VfmE is required for c-di-GMP binding, and mutation of the motif in the mutant R93D eliminates the c-di-GMP-related phenotypes in Pel activity. We propose that VfmE is an important mediator to integrate quorum-sensing signals with c-di-GMP to collectively regulate D. dadantii pathogenesis.

The RNA-Binding Protein ProQ Impacts Exopolysaccharide Biosynthesis and Second Messenger Cyclic di-GMP Signaling in the Fire Blight Pathogen Erwinia amylovora

Erwinia amylovora is a plant-pathogenic bacterium that causes fire blight disease in many economically important plants, including apples and pears. This bacterium produces three exopolysaccharides (EPSs), amylovoran, levan, and cellulose, and forms biofilms in host plant vascular tissues, which are crucial for pathogenesis. Here, we demonstrate that ProQ, a conserved bacterial RNA chaperone, was required for the virulence of E. amylovora in apple shoots and for biofilm formation in planta. In vitro experiments revealed that the deletion of proQ increased the production of amylovoran and cellulose. Prc is a putative periplasmic protease, and the prc gene is located adjacent to proQ. We found that Prc and the associated lipoprotein NlpI negatively affected amylovoran production, whereas Spr, a peptidoglycan hydrolase degraded by Prc, positively regulated amylovoran. Since the prc promoter is likely located within proQ, our data showed that proQ deletion significantly reduced the prc mRNA levels. We used a genome-wide transposon mutagenesis experiment to uncover the involvement of the bacterial second messenger c-di-GMP in ProQ-mediated cellulose production. The deletion of proQ resulted in elevated intracellular c-di-GMP levels and cellulose production, which were restored to wild-type levels by deleting genes encoding c-di-GMP biosynthesis enzymes. Moreover, ProQ positively affected the mRNA levels of genes encoding c-di-GMP-degrading phosphodiesterase enzymes via a mechanism independent of mRNA decay. In summary, our study revealed a detailed function of E. amylovora ProQ in coordinating cellulose biosynthesis and, for the first time, linked ProQ with c-di-GMP metabolism and also uncovered a role of Prc in the regulation of amylovoran production. IMPORTANCE Fire blight, caused by the bacterium Erwinia amylovora, is an important disease affecting many rosaceous plants, including apple and pear, that can lead to devastating economic losses worldwide. Similar to many xylem-invading pathogens, E. amylovora forms biofilms that rely on the production of exopolysaccharides (EPSs). In this paper, we identified the RNA-binding protein ProQ as an important virulence regulator. ProQ played a central role in controlling the production of EPSs and participated in the regulation of several conserved bacterial signal transduction pathways, including the second messenger c-di-GMP and the periplasmic protease Prc-mediated systems. Since ProQ has recently been recognized as a global posttranscriptional regulator in many bacteria, these findings provide new insights into multitiered regulatory mechanisms for the precise control of virulence factor production in bacterial pathogens.

A Novel Signaling Pathway Connects Thiamine Biosynthesis, Bacterial Respiration, and Production of the Exopolysaccharide Amylovoran in Erwinia amylovora

Erwinia amylovora is a plant pathogen causing necrotrophic fire blight disease of apple, pear, and other rosaceous plants. This bacterium colonizes host vascular tissues via the production of exopolysaccharides (EPSs) including amylovoran. It is well-established that the nearly ubiquitous plasmid pEA29 of E. amylovora is an essential virulence factor, but the underlying mechanism remains uncharacterized. Here, we demonstrated that pEA29 was required for E. amylovora to produce amylovoran and to form a biofilm, and this regulation was dependent on the thiamine biosynthesis operon thiOSGF. We then conducted carbohydrate and genetic analyses demonstrating that the thiamine-mediated effect on amylovoran production was indirect, as cells lacking thiOSGF produced an EPS that did not contain glucuronic acid, one of the key components of amylovoran, whereas the transcriptional activity and RNA levels of the amylovoran biosynthesis genes were not altered. Alternatively, addition of exogenous thiamine restored amylovoran production in the pEA29-cured strain of E. amylovora and positively impacted amylovoran production in a dose-dependent manner. Individual deletion of several chromosomal thiamine biosynthesis genes also affected amylovoran production, implying that a complete thiamine biosynthesis pathway is required for the thiamine-mediated effect on amylovoran production in E. amylovora. Finally, we determined that an imbalanced tricarboxylic acid cycle negatively affected amylovoran production, which was restored by addition of exogenous thiamine or overexpression of the thiOSGF operon. In summary, our report revealed a novel signaling pathway that impacts E. amylovora virulence in which thiamine biosynthesis enhances bacterial respiration that provides energetic requirements for the biosynthesis of EPS amylovoran.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Sequence polarity between the promoter and the adjacent gene modulates promoter activity

Promoter engineering has been employed as a strategy to enhance and optimize the production of bio-products. Availability of promoters with predictable activities is needed for downstream application. However, whether promoter activity remains the same in different gene contexts remains unknown. Six consecutive promoters that have previously been determined to have different activity levels were used to construct six different versions of plasmid backbone pTH1227, followed by inserted genes encoding two polymer-producing enzymes. In some cases, promoter activity in the presence of inserted genes did not correspond to the reported activity levels in a previous study. After removing the inserted genes, the activity of these promoters returned to their previously reported level. These changes were further confirmed to occur at the transcriptional level. Polymer production using our newly constructed plasmids showed polymer accumulation levels corresponding to the promoter activity reported in our study. Our study demonstrated the importance of re-assessing promoter activity levels with regard to gene context, which could influence promoter activity, leading to different outcomes in downstream applications.

Fluorescent Protein Expression as a Proxy for Bacterial Fitness in a High-Throughput Assay

Bacterial growth is classically assessed by measuring the increases in optical density of pure cultures in shaken liquid media. Measuring growth using optical density has severe limitations when studying multistrain interactions, as it is not possible to measure the growth of individual strains within mixed cultures. Here, we demonstrated that constitutively expressed fluorescent proteins can be used to track the growth of individual strains in different liquid media. Fluorescence measurements were highly correlated with optical density measurements and cell counts. This allowed us to assess bacterial growth not only in pure cultures but also in mixed bacterial cultures and determine the impact of a competitor on a focal strain, thereby assessing relative fitness. Furthermore, we were able to track the growth of two different strains simultaneously by using fluorescent proteins with differential excitation and emission wavelengths. Bacterial densities measured by fluorescence yielded more consistent data between technical replicates than optical density measurements. Our setup employs fluorescence microplate readers that allow high throughput and replication. IMPORTANCE We expand on an important limitation of the concept of measuring bacterial growth, which is classically limited to one strain at a time. By adopting our approach, it is possible to measure the growth of several bacterial strains simultaneously with high temporal resolution and in a high-throughput manner. This is important to investigate bacterial interactions, such as competition and facilitation.

Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design

Simple Summary

Synthetic biology is an emerging discipline, offering new perspectives in many industrial fields, from pharma and row-material production to renewable energy. Developing synthetic biology applications is often a lengthy and expensive process with extensive and tedious trial-and-error runs. Computational models can direct the engineering of biological circuits in a computer-aided design setting. By providing a virtual lab environment, in silico models of synthetic circuits can contribute to a quantitative understanding of the underlying molecular pathways before a wet-lab implementation. Here, we illustrate this notion from the point of view of signal fidelity and noise relationship. Noise in gene expression can undermine signal fidelity with implications on the well-functioning of the engineered organisms. For our analysis, we use a specific biological circuit that regulates the gene expression in bacterial inorganic phosphate economy. Applications that use this circuit include those in pollutant detection and wastewater treatment. We provide computational models with different levels of molecular detail as virtual labs. We show that inherent fluctuations in the gene expression machinery can be predicted via stochastic simulations to introduce control in the synthetic promoter design process. Our analysis suggests that noise in the system can be alleviated by strong synthetic promoters with slow unbinding rates. Overall, we provide a recipe for the computer-aided design of synthetic promoter libraries with specific signal to noise characteristics.

Abstract

The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.

Magnetic induction inspires a schematic theory for crosstalk-driven relaxation dynamics in cells

Establishing formal mathematical analogies between disparate physical systems can be a powerful tool, allowing for the well studied behavior of one system to be directly translated into predictions about the behavior of another that may be harder to probe. In this paper we lay the foundation for such an analogy between the macroscale electrodynamics of simple magnetic circuits and the microscale chemical kinetics of transcriptional regulation in cells. By artificially allowing the inductor coils of the former to elastically expand under the action of their Lorentz pressure, we introduce nonlinearities into the system that we interpret through the lens of our analogy as a schematic model for the impact of crosstalk on the rates of gene expression near steady state. Synthetic plasmids introduced into a cell must compete for a finite pool of metabolic and enzymatic resources against a maelstrom of crisscrossing biological processes, and our theory makes sensible predictions about how this noisy background might impact the expression profiles of synthetic constructs without explicitly modeling the kinetics of numerous interconnected regulatory interactions. We conclude the paper with a discussion of how our theory might be expanded to a broader class of plasmid circuits and how our predictions might be tested experimentally.

A new analysis method for evaluating bacterial growth with microplate readers

Growth curve measurements are commonly used in microbiology, while the use of microplate readers for such measurements provides better temporal resolution and higher throughput. However, evaluating bacterial growth with microplate readers has been hurdled by barriers such as multiple scattering. Here, we report our development of a method based on the time derivatives of the optical density (OD) and/or fluorescence (FL) of bacterial cultures to overcome these barriers. First, we illustrated our method using quantitative models and numerical simulations, which predicted the number of bacteria and the number of fluorescent proteins in time as well as their time derivatives. Then, we systematically investigated how the time derivatives depend on the parameters in the models/simulations, providing a framework for understanding the FL growth curves. In addition, as a demonstration, we applied our method to study the lag time elongation of bacteria subjected to treatment with silver (Ag⁺) ions and found that the results from our method corroborated well with that from growth curve fitting by the Gompertz model that has been commonly used in the literature. Furthermore, this method was applied to the growth of bacteria in the presence of silver nanoparticles (AgNPs) at various concentrations, where the OD curve measurements failed. We showed that our method allowed us to successfully extract the growth behavior of the bacteria from the FL measurements and understand how the growth was affected by the AgNPs.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Leaf Surface Topography Contributes to the Ability of Escherichia coli on Leafy Greens to Resist Removal by Washing, Escape Disinfection With Chlorine, and Disperse Through Splash

The attachment of foodborne pathogens to leaf surfaces is a complex process that involves multiple physical, chemical, and biological factors. Here, we report the results from a study designed to specifically determine the contribution of spinach leaf surface topography as it relates to leaf axis (abaxial and adaxial) and leaf age (15, 45, and 75 days old) to the ability of Escherichia coli to resist removal by surface wash, to avoid inactivation by chlorine, and to disperse through splash impact. We used fresh spinach leaves, as well as so-called "replicasts" of spinach leaf surfaces in the elastomer polydimethylsiloxane to show that leaf vein density correlated positively with the failure to recover E. coli from surfaces, not only using a simple water wash and rinse, but also a more stringent wash protocol involving a detergent. Such failure was more pronounced when E. coli was surface-incubated at 24°C compared to 4°C, and in the presence, rather than absence, of nutrients. Leaf venation also contributed to the ability of E. coli to survive a 50 ppm available chlorine wash and to laterally disperse by splash impact. Our findings suggest that the topographical properties of the leafy green surface, which vary by leaf age and axis, may need to be taken into consideration when developing prevention or intervention strategies to enhance the microbial safety of leafy greens.

Universal Actuator for Efficient Silencing of Escherichia coli Genes Based on Convergent Transcription Resistant to Rho-Dependent Termination

Dynamic control is a distinguished strategy in modern metabolic engineering, in which inducible convergent transcription is an attractive approach for conditional gene silencing. Instead of a simple strong "reverse" (r-) promoter, a three-component actuator has been developed for constitutive genes silencing. These actuators, consisting of r-promoters with different strengths, the ribosomal transcription antitermination-inducing sequence rrnG-AT, and the RNase III processing site, were inserted into the 3'-UTR of three E. coli metabolic genes. Second and third actuator components were important to improve the effectiveness and robustness of the approach. The maximal silencing folds achieved for gltA, pgi, and ppc were approximately 7, 11, and >100, respectively. Data were analyzed using a simple model that considered RNA polymerase (RNAP) head-on collisions as the unique reason for gene silencing and continued transcription after collision with only one of two molecules. It was previously established that forward (f-) RNAP with a trailing ribosome was approximately 13-times more likely to continue transcription after head-on collision than untrailed r-RNAP which is sensitive to Rho-dependent transcription termination (RhoTT). According to the current results, this bias in complex stabilities decreased to no more than (3.0-5.7)-fold if r-RNAP became resistant to RhoTT. Therefore, the developed constitutive actuator could be considered as an improved tool for controlled gene expression mainly due to the transfer of r-transcription into a state that is resistant to potential termination and used as the basis for the design of tightly regulated actuators for the achievement of conditional silencing.

Data-driven statistical modeling of the emergent behavior of biohybrid microrobots

Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms-NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.

Correlation of mRNA delivery timing and protein expression in lipid-based transfection

Non-viral gene delivery is constrained by the dwell time that most synthetic nucleic acid nanocarriers spend inside endosomal compartments. In order to overcome this endosomal-release bottleneck, methods are required that measure nanocarrier uptake kinetics and transfection efficiency simultaneously. Here, we employ live-cell imaging on single-cell arrays (LISCA) to study the delivery-time distribution of lipid-based mRNA complexes under varied serum conditions. By fitting a translation-maturation model to hundreds of individual eGFP reporter fluorescence time courses, the protein expression onset times and the expression rates after transfection are determined. Using this approach, we find that delivery timing and protein expression rates are not intrinsically correlated at the single-cell level, even though population-averaged values of both parameters conjointly change as a function of increasing external serum protein fraction. Lipofectamine-mediated delivery showed decreased transfection efficiency and longer delivery times with increasing serum protein concentration. This is in contrast to ionizable lipid nanoparticle (i-LNP)-mediated transfer, which showed increased efficiency and faster uptake in the presence of serum. In conclusion, the interdependences of single-cell expression rates and onset timing provide additional clues on uptake and release mechanisms, which are useful for improving nucleic acid delivery.

Manipulating rod-shaped bacteria with optical tweezers

Optical tweezers have great potential in microbiology for holding and manipulating single cells under a microscope. However, the methodology to use optical tweezers for live cell studies is still at its infancy. In this work, we determined suitable parameters for stable trapping of single Escherichia coli bacteria, and identified the upper limits of IR-exposure that can be applied without affecting viability. We found that the maximum tolerable IR-exposure is 2.5-fold higher when employing oscillating instead of stationary optical trapping (20 J and 8 J, respectively). We found that good stability of cells in an oscillating trap is achieved when the effective trap length is 20% larger than the cell length, the oscillation frequency higher than 100 Hz and the trap oriented perpendicular to the medium flow direction. Further, we show, using an IR power just sufficient for stable holding, that bacteria remain viable during at least 30 min of holding in an oscillating trap. In this work, we established a method for long-term stable handling of single E. coli cells using optical tweezers. This work will pave the way for future use of optical tweezers in microbiology.

Multisite phosphorylation drives phenotypic variation in (p)ppGpp synthetase-dependent antibiotic tolerance

Isogenic populations of cells exhibit phenotypic variability that has specific physiological consequences. Individual bacteria within a population can differ in antibiotic tolerance, but whether this variability can be regulated or is generally an unavoidable consequence of stochastic fluctuations is unclear. Here we report that a gene encoding a bacterial (p)ppGpp synthetase in Bacillus subtilis, sasA, exhibits high levels of extrinsic noise in expression. We find that sasA is regulated by multisite phosphorylation of the transcription factor WalR, mediated by a Ser/Thr kinase-phosphatase pair PrkC/PrpC, and a Histidine kinase WalK of a two-component system. This regulatory intersection is crucial for controlling the appearance of outliers; rare cells with unusually high levels of sasA expression, having increased antibiotic tolerance. We create a predictive model demonstrating that the probability of a given cell surviving antibiotic treatment increases with sasA expression. Therefore, multisite phosphorylation can be used to strongly regulate variability in antibiotic tolerance.

WellInverter: a web application for the analysis of fluorescent reporter gene data

Background

Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion.

Results

We present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene.

Conclusions

The new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2920-4) contains supplementary material, which is available to authorized users.

Decoding the Time-Dependent Response of Bioluminescent Metal-Detecting Whole-Cell Bacterial Sensors

The signal produced by aqueous dispersions of bioluminescent, metal-responsive whole-cell bacterial sensors is indicative of the concentration of bioavailable metal ions in solution. The conventional calibration-based strategy followed for measuring this concentration is however inadequate to provide any quantitative prediction of the cell response over time as a function of, e.g., their growth features, their defining metal accumulation properties, or the physicochemical medium composition. Such an evaluation is still critically needed for assessing on a mechanistic level the performance of biosensors in terms of metal bioavailability and toxicity monitoring. Herein we report a comprehensive formalism unraveling how the dependence of bioluminescence on time is governed by the dynamics of metal biouptake, by the activation kinetics of lux-based reporter gene, and by the ensuing rate of luciferase production, the kinetics of light emission, and quenching. It is shown that the bioluminescence signal corresponds to the convolution product between two time-dependent functions, one detailing the dynamic interplay of the above micro- and nanoscale processes, and the other pertaining to the change in concentration of photoactive cell sensors over time. Numerical computations illustrate how the shape and magnitude of the bioluminescence peak(s) are intimately connected to the dependence of the photoactive cell concentration on time and to the magnitudes of Deborah numbers that compare the relevant time scales of the biointerfacial and intracellular events controlling light emission. Explicit analytical expressions are further derived for practical situations where bioluminescence is proportional to the concentration of metal ions in solution. The theory is further quantitatively supported by experiments performed on luminescent cadmium-responsive lux-based Escherichia coli biosensors.

A feed-forward signalling circuit controls bacterial virulence through linking cyclic di-GMP and two mechanistically distinct sRNAs, ArcZ and RsmB

Dickeya dadantii is a plant pathogen that causes soft rot disease on vegetable and potato crops. To successfully cause infection, this pathogen needs to coordinately modulate the expression of genes encoding several virulence determinants, including plant cell wall degrading enzymes (PCWDEs), type III secretion system (T3SS) and flagellar motility. Here, we uncover a novel feed-forward signalling circuit for controlling virulence. Global RNA chaperone Hfq interacts with an Hfq-dependent sRNA ArcZ and represses the translation of pecT, encoding a LysR-type transcriptional regulator. We demonstrate that the ability of ArcZ to be processed to a 50 nt 3'- end fragment is essential for its regulation of pecT. PecT down-regulates PCWDE and the T3SS by repressing the expression of a global post-transcriptional regulator- (RsmA-) associated sRNA encoding gene rsmB. In addition, we show that the protein levels of two cyclic di-GMP (c-di-GMP) diguanylate cyclases (DGCs), GcpA and GcpL, are repressed by Hfq. Further studies show that both DGCs are essential for the Hfq-mediated post-transcriptional regulation on RsmB. Overall, our report provides new insights into the interplays between ubiquitous signalling transduction systems that were most studied independently and sheds light on multitiered regulatory mechanisms for a precise disease regulation in bacteria.

CRISPR-assisted multi-dimensional regulation for fine-tuning gene expression in Bacillus subtilis

Fine-tuning of gene expression is crucial for protein expression and pathway construction, but it still faces formidable challenges due to the hierarchical gene regulation at multiple levels in a context-dependent manner. In this study, we defined the optimal targeting windows for CRISPRa and CRISPRi of the dCas9-α/ω system, and demonstrated that this system could act as a single master regulator to simultaneously activate and repress the expression of different genes by designing position-specific gRNAs. The application scope of dCas9-ω was further expanded by a newly developed CRISPR-assisted Oligonucleotide Annealing based Promoter Shuffling (OAPS) strategy, which could generate a high proportion of functional promoter mutants and facilitate the construction of effective promoter libraries in microorganisms with low transformation efficiency. Combing OAPS and dCas9-ω, the influences of promoter-based transcription, molecular chaperone-assisted protein folding and protease-mediated degradation on the expression of amylase BLA in Bacillus subtilis were systematically evaluated, and a 260-fold enhancement of BLA production was obtained. The success of the OAPS strategy and dCas9-ω for BLA production in this study thus demonstrated that it could serve as a powerful tool kit to regulate the expression of multiple genes multi-directionally and multi-dimensionally in bacteria.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures

Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved population heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subpopulations were found with some cells that seem to be more robust than the rest of the population. In conclusion, following batch cultures population heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which population traits to aim for.

Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era

BACKGROUND

Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production.

RESULTS

In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R ² values more than 0.7 and 0.9, respectively.

CONCLUSIONS

This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.

Expression and regulation of the Pseudomonas aeruginosa hibernation promoting factor

Bacterial biofilms contain subpopulations of cells that are dormant and highly tolerant to antibiotics. While dormant, the bacteria must maintain the integrity of macromolecules required for resuscitation. Previously, we showed that hibernation promoting factor (HPF) is essential for protecting Pseudomonas aeruginosa from ribosomal loss during dormancy. In this study, we mapped the genetic components required for hpf expression. Using 5'-RACE and fluorescent protein reporter fusions, we show that hpf is expressed as part of the rpoN operon, but that hpf also has a second promoter (P_hpf ) within the rpoN gene. P_hpf is active when the cells enter stationary phase, and expression from P_hpf is modulated, but not eliminated, in mutant strains impaired in stationary phase transition (ΔdksA2, ΔrpoS and ΔrelA/ΔspoT mutants). The results of reporter gene studies and mRNA folding predictions indicated that the 5' end of the hpf mRNA may also influence hpf expression. Mutations that opened or that stabilized the mRNA hairpin loop structures strongly influenced the amount of HPF produced. The results demonstrate that hpf is expressed independently of rpoN, and that hpf regulation includes both transcriptional and post-transcriptional processes, allowing the cells to produce sufficient HPF during stationary phase to maintain viability while dormant.

Development of a Method to Monitor Gene Expression in Single Bacterial Cells During the Interaction With Plants and Use to Study the Expression of the Type III Secretion System in Single Cells of Dickeya dadantii in Potato

Dickeya dadantii is a bacterial plant pathogen that causes soft rot disease on a wide range of host plants. The type III secretion system (T3SS) is an important virulence factor in D. dadantii. Expression of the T3SS is induced in the plant apoplast or in hrp-inducing minimal medium (hrp-MM), and is repressed in nutrient-rich media. Despite the understanding of induction conditions, how individual cells in a clonal bacterial population respond to these conditions and modulate T3SS expression is not well understood. In our previous study, we reported that in a clonal population, only a small proportion of bacteria highly expressed T3SS genes while the majority of the population did not express T3SS genes under hrp-MM condition. In this study, we developed a method that enabled in situ observation and quantification of gene expression in single bacterial cells in planta. Using this technique, we observed that the expression of the T3SS genes hrpA and hrpN is restricted to a small proportion of D. dadantii cells during the infection of potato. We also report that the expression of T3SS genes is higher at early stages of infection compared to later stages. This expression modulation is achieved through adjusting the ratio of T3SS ON and T3SS OFF cells and the expression intensity of T3SS ON cells. Our findings not only shed light into how bacteria use a bi-stable gene expression manner to modulate an important virulence factor, but also provide a useful tool to study gene expression in individual bacterial cells in planta.

Quantitative Investigation of the Role of Intra-/Intercellular Dynamics in Bacterial Quorum Sensing

Bacteria utilize diffusible signals to regulate population density-dependent coordinated gene expression in a process called quorum sensing (QS). While the intracellular regulatory mechanisms of QS are well-understood, the effect of spatiotemporal changes in the population configuration on the sensitivity and robustness of the QS response remains largely unexplored. Using a microfluidic device, we quantitatively characterized the emergent behavior of a population of swimming E. coli bacteria engineered with the lux QS system and a GFP reporter. We show that the QS activation time follows a power law with respect to bacterial population density, but this trend is disrupted significantly by microscale variations in population configuration and genetic circuit noise. We then developed a computational model that integrates population dynamics with genetic circuit dynamics to enable accurate (less than 7% error) quantitation of the bacterial QS activation time. Through modeling and experimental analyses, we show that changes in spatial configuration of swimming bacteria can drastically alter the QS activation time, by up to 22%. The integrative model developed herein also enables examination of the performance robustness of synthetic circuits with respect to growth rate, circuit sensitivity, and the population's initial size and spatial structure. Our framework facilitates quantitative tuning of microbial systems performance through rational engineering of synthetic ribosomal binding sites. We have demonstrated this through modulation of QS activation time over an order of magnitude. Altogether, we conclude that predictive engineering of QS-based bacterial systems requires not only the precise temporal modulation of gene expression (intracellular dynamics) but also accounting for the spatiotemporal changes in population configuration (intercellular dynamics).

Reconstructing Dynamic Promoter Activity Profiles from Reporter Gene Data

Accurate characterization of promoter activity is important when designing expression systems for systems biology and metabolic engineering applications. Promoters that respond to changes in the environment enable the dynamic control of gene expression without the necessity of inducer compounds, for example. However, the dynamic nature of these processes poses challenges for estimating promoter activity. Most experimental approaches utilize reporter gene expression to estimate promoter activity. Typically the reporter gene encodes a fluorescent protein that is used to infer a constant promoter activity despite the fact that the observed output may be dynamic and is a number of steps away from the transcription process. In fact, some promoters that are often thought of as constitutive can show changes in activity when growth conditions change. For these reasons, we have developed a system of ordinary differential equations for estimating dynamic promoter activity for promoters that change their activity in response to the environment that is robust to noise and changes in growth rate. Our approach, inference of dynamic promoter activity (PromAct), improves on existing methods by more accurately inferring known promoter activity profiles. This method is also capable of estimating the correct scale of promoter activity and can be applied to quantitative data sets to estimate quantitative rates.

Dynamic Measures of Flagellar Gene Expression

Many genes are required to assemble flagella. These genes encode not only the structural elements of the flagellum but also a number of regulators that control how the flagellar genes are temporally expressed during the assembly process. These regulators also specify the likelihood that a given cell will express the flagellar genes. In particular, not all cells express the flagellar genes, resulting in mixed populations of motile and non-motile cells. Nutrients provide one signal that specifies the motile fraction. In this chapter, we describe two methods for measuring flagellar gene expression dynamics using fluorescent proteins in Salmonella enterica. Both the methods can be used to investigate the mechanisms governing flagellar gene expression dynamics.

The diguanylate cyclase GcpA inhibits the production of pectate lyases via the H-NS protein and RsmB regulatory RNA in Dickeya dadantii

Dickeya dadantii 3937 secretes pectate lyases (Pels) to degrade plant cell walls. Previously, we have demonstrated that EGcpB and EcpC function as bis-(3',5')-cyclic dimeric guanosine monophosphate (c-di-GMP)-specific phosphodiesterases (PDEs) to positively regulate Pel production. However, the diguanylate cyclase (DGC) responsible for the synthesis of c-di-GMP and the dichotomous regulation of Pel has remained a mystery. Here, we identified GcpA as the dominant DGC to negatively regulate Pel production by the specific repression of pelD gene expression. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assays revealed that the expression levels of histone-like, nucleoid-structuring protein encoding gene hns and post-transcriptional regulator encoding genes rsmA and rsmB were significantly affected by GcpA. Deletion of hns or rsmB in the gcpAD418A site-directed mutant restored its Pel production and pelD expression, demonstrating that H-NS and RsmB contribute to the GcpA-dependent regulation of Pel in D. dadantii. In addition, RsmB expression was subject to positive regulation by H-NS. Thus, we propose a novel pathway consisting of GcpA-H-NS-RsmB-RsmA-pelD that controls Pel production in D. dadantii. Furthermore, we showed that H-NS and RsmB are responsible for the GcpA-dependent regulation of motility and type III secretion system (T3SS) gene expression, respectively. Of the two PDEs involved in the regulation of Pels, only EGcpB regulates pelD expression through the same pathway as GcpA.

Model-based design of RNA hybridization networks implemented in living cells

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations.

Deconstructing a multiple antibiotic resistance regulation through the quantification of its input function

Many essential bacterial responses present complex transcriptional regulation of gene expression. To what extent can the study of these responses substantiate the logic of their regulation? Here, we show how the input function of the genes constituting the response, i.e., the information of how their transcription rates change as function of the signals acting on the regulators, can serve as a quantitative tool to deconstruct the corresponding regulatory logic. To demonstrate this approach, we consider the multiple antibiotic resistance (mar) response in Escherichia coli. By characterizing the input function of its representative genes in wild-type and mutant bacteria, we recognize a dual autoregulation motif as main determinant of the response, which is further adjusted by the interplay with other regulators. We show that basic attributes, like its reaction to a wide range of stress or its moderate expression change, are associated with a strong negative autoregulation, while others, like the buffering of metabolic signals or the lack of memory to previous stress, are related to a weak positive autoregulation. With a mathematical model of the input functions, we identify some constraints fixing the molecular attributes of the regulators, and also notice the relevance of the bicystronic architecture harboring the dual autoregulation that is unique in E. coli. The input function emerges then as a tool to disentangle the rationale behind most of the attributes defining the mar phenotype. Overall, the present study supports the value of characterizing input functions to deconstruct the complexity of regulatory architectures in prokaryotic and eukaryotic systems.

Many cellular responses result from the integration of numerous regulatory signals. To deconstruct the regulation of one of these responses, which enables resistance to multiple antibiotics in Escherichia coli, a team led by Juan F. Poyatos at the National Center for Biotechnology in Madrid studied the response input function by combining theoretical models and experiments. This function quantifies the rate of transcription of the genes constituting the response with respect to the signals acting on its cognate regulators. By examining how the shape of the function changes in different situations, e.g., when a given regulator is mutated, the team identified the implications for the specificity and dynamics of the response of a dual autoregulation at the core of the control architecture. The use of input functions as quantitative tools allows us to reverse engineer the complex regulations that dictate essential physiological functions in both prokaryotic and eukaryotic cells.

MazF activation promotes translational heterogeneity of the grcA mRNA in Escherichia coli populations

Bacteria adapt to adverse environmental conditions by altering gene expression patterns. Recently, a novel stress adaptation mechanism has been described that allows Escherichia coli to alter gene expression at the post-transcriptional level. The key player in this regulatory pathway is the endoribonuclease MazF, the toxin component of the toxin-antitoxin module mazEF that is triggered by various stressful conditions. In general, MazF degrades the majority of transcripts by cleaving at ACA sites, which results in the retardation of bacterial growth. Furthermore, MazF can process a small subset of mRNAs and render them leaderless by removing their ribosome binding site. MazF concomitantly modifies ribosomes, making them selective for the translation of leaderless mRNAs. In this study, we employed fluorescent reporter-systems to investigate mazEF expression during stressful conditions, and to infer consequences of the mRNA processing mediated by MazF on gene expression at the single-cell level. Our results suggest that mazEF transcription is maintained at low levels in single cells encountering adverse conditions, such as antibiotic stress or amino acid starvation. Moreover, using the grcA mRNA as a model for MazF-mediated mRNA processing, we found that MazF activation promotes heterogeneity in the grcA reporter expression, resulting in a subpopulation of cells with increased levels of GrcA reporter protein.

Stretchable living materials and devices with hydrogel-elastomer hybrids hosting programmed cells

Living systems, such as bacteria, yeasts, and mammalian cells, can be genetically programmed with synthetic circuits that execute sensing, computing, memory, and response functions. Integrating these functional living components into materials and devices will provide powerful tools for scientific research and enable new technological applications. However, it has been a grand challenge to maintain the viability, functionality, and safety of living components in freestanding materials and devices, which frequently undergo deformations during applications. Here, we report the design of a set of living materials and devices based on stretchable, robust, and biocompatible hydrogel-elastomer hybrids that host various types of genetically engineered bacterial cells. The hydrogel provides sustainable supplies of water and nutrients, and the elastomer is air-permeable, maintaining long-term viability and functionality of the encapsulated cells. Communication between different bacterial strains and with the environment is achieved via diffusion of molecules in the hydrogel. The high stretchability and robustness of the hydrogel-elastomer hybrids prevent leakage of cells from the living materials and devices, even under large deformations. We show functions and applications of stretchable living sensors that are responsive to multiple chemicals in a variety of form factors, including skin patches and gloves-based sensors. We further develop a quantitative model that couples transportation of signaling molecules and cellular response to aid the design of future living materials and devices.

Molecular noise can minimize the collective sensitivity of a clonal heterogeneous cell population

It is now widely accepted that molecular noise, rather than be always detrimental, introduces in many circumstances the required boost to reach fundamental cellular activities or strategies otherwise unattainable. In threshold-like genetic systems, molecular noise serves to generate heterogeneous responses in a clonal population, also in a tissue, due to cell-to-cell variability. Here, we derived a mathematical framework from which we could study in detail this effect. We focused on a minimal decision-making gene circuit implemented as a transcriptional positive-feedback loop. We evidenced that when the individual responses of each cell within the population are averaged, a sort of collective behavior, the resulting dose-response curve is linearized. In other words, the population is less sensitive than the individuals, which otherwise enhances the information transfer from signal to response. We found that the distance to the ideal linear response of the cell population is minimized for a particular noise level, and also characterized the interplay between intrinsic and extrinsic noise. Overall, our results highlight how cells could, by acting as a collective, entangle their genetic systems with their environments by adjusting the intracellular noise levels.

Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries

Bacterial communities undergo collective behavioural switches upon producing and sensing diffusible signal molecules; a mechanism referred to as Quorum Sensing (QS). Exemplarily, biofilm organic matrices are built concertedly by bacteria in several environments. QS scope in bacterial ecology has been debated for over 20 years. Different perspectives counterpose the role of density reporter for populations to that of local environment diffusivity probe for individual cells. Here we devise a model system where tubes of different heights contain matrix-embedded producers and sensors. These tubes allow non-limiting signal diffusion from one open end, thereby showing that population spatial extension away from an open boundary can be a main critical factor in QS. Experimental data, successfully recapitulated by a comprehensive mathematical model, demonstrate how tube height can overtake the role of producer density in triggering sensor activation. The biotic degradation of the signal is found to play a major role and to be species-specific and entirely feedback-independent.

Evaluation of promoters and ribosome binding sites for biotechnological applications in the unicellular cyanobacterium Synechocystis sp. PCC 6803

For effective metabolic engineering, a toolbox of genetic components that enables predictable control of gene expression is needed. Here we present a systematic study of promoters and ribosome binding sites in the unicellular cyanobacterium Synechocystis sp. PCC 6803. A set of metal ion inducible promoters from Synechocystis were compared to commonly used constitutive promoters, by measuring fluorescence of a reporter protein in a standardized setting to allow for accurate comparisons of promoter activity. The most versatile and useful promoter was found to be PnrsB, which from a relatively silent expression could be induced almost 40-fold, nearly up to the activity of the strong psbA2 promoter. By varying the concentrations of the two metal ion inducers Ni2+ and Co2+, expression from the promoter was highly tunable, results that were reproduced with PnrsB driving ethanol production. The activities of several ribosomal binding sites were also measured, and tested in parallel in Synechocystis and Escherichia coli. The results of the study add useful information to the Synechocystis genetic toolbox for biotechnological applications.

Fluctuation sensitivity of a transcriptional signaling cascade

The internal biochemical state of a cell is regulated by a vast transcriptional network that kinetically correlates the concentrations of numerous proteins. Fluctuations in protein concentration that encode crucial information about this changing state must compete with fluctuations caused by the noisy cellular environment in order to successfully transmit information across the network. Oftentimes, one protein must regulate another through a sequence of intermediaries, and conventional wisdom, derived from the data processing inequality of information theory, leads us to expect that longer sequences should lose more information to noise. Using the metric of mutual information to characterize the fluctuation sensitivity of transcriptional signaling cascades, we find, counter to this expectation, that longer chains of regulatory interactions can instead lead to enhanced informational efficiency. We derive an analytic expression for the mutual information from a generalized chemical kinetics model that we reduce to simple, mass-action kinetics by linearizing for small fluctuations about the basal biological steady state, and we find that at long times this expression depends only on a simple ratio of protein production to destruction rates and the length of the cascade. We place bounds on the values of these parameters by requiring that the mutual information be at least one bit-otherwise, any received signal would be indistinguishable from noise-and we find not only that nature has devised a way to circumvent the data processing inequality, but that it must be circumvented to attain this one-bit threshold. We demonstrate how this result places informational and biochemical efficiency at odds with one another by correlating high transcription factor binding affinities with low informational output, and we conclude with an analysis of the validity of our assumptions and propose how they might be tested experimentally.