Effects of growth rate and promoter activity on single-cell protein expression

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.

Photo-addressable microwell devices for rapid functional screening and isolation of pathogen inhibitors from bacterial strain libraries

Discovery of new strains of bacteria that inhibit pathogen growth can facilitate improvements in biocontrol and probiotic strategies. Traditional, plate-based co-culture approaches that probe microbial interactions can impede this discovery as these methods are inherently low-throughput, labor-intensive, and qualitative. We report a second-generation, photo-addressable microwell device, developed to iteratively screen interactions between candidate biocontrol agents existing in bacterial strain libraries and pathogens under increasing pathogen pressure. Microwells (0.6 pl volume) provide unique co-culture sites between library strains and pathogens at controlled cellular ratios. During sequential screening iterations, library strains are challenged against increasing numbers of pathogens to quantitatively identify microwells containing strains inhibiting the highest numbers of pathogens. Ring-patterned 365 nm light is then used to ablate a photodegradable hydrogel membrane and sequentially release inhibitory strains from the device for recovery. Pathogen inhibition with each recovered strain is validated, followed by whole genome sequencing. To demonstrate the rapid nature of this approach, the device was used to screen a 293-membered biovar 1 agrobacterial strain library for strains inhibitory to the plant pathogen Agrobacterium tumefaciens sp. 15955. One iterative screen revealed nine new inhibitory strains. For comparison, plate-based methods did not uncover any inhibitory strains from the library (n = 30 plates). The novel pathogen-challenge screening mode developed here enables rapid selection and recovery of strains that effectively suppress pathogen growth from bacterial strain libraries, expanding this microwell technology platform toward rapid, cost-effective, and scalable screening for probiotics, biocontrol agents, and inhibitory molecules that can protect against known or emerging pathogens.

Comparison of Transcription Elongation Rates of Three RNA Polymerases in Real Time

RNA polymerases (RNAPs) across the bacterial kingdom have retained a conserved structure and function. In spite of the remarkable similarity of the enzyme in different bacteria, a wide variation is found in the promoter-polymerase interaction, transcription initiation, and termination. However, the transcription elongation was considered to be a monotonic process, although the rate of elongation could vary in different bacteria. Such variations in RNAP elongation rates could be important to fine-tune the transcription, which in turn would influence cellular metabolism and growth rates. Here, we describe a quantitative study to measure the transcription rates for the RNAPs from three bacteria, namely, Mycobacterium tuberculosis, Mycobacterium smegmatis, and Escherichia coli, which exhibit different growth kinetics. The RNA synthesis rates of the RNAPs were calculated from the real-time elongation kinetic profile using surface plasmon resonance through a computational flux flow model. The computational model revealed the modular process of elongation, with different rate profiles for the three RNAPs. Notably, the transcription elongation rates of these RNAPs followed the trend in the growth rates of these bacteria.

Reliable online measurement of population dynamics for filamentous co-cultures

Understanding population dynamics is a key factor for optimizing co-culture processes to produce valuable compounds. However, the measurement of independent population dynamics is difficult, especially for filamentous organisms and in presence of insoluble substrates like cellulose. We propose a workflow for fluorescence-based online monitoring of individual population dynamics of two filamentous microorganisms. The fluorescent tagged target co-culture is composed of the cellulolytic fungus Trichoderma reesei RUT-C30-mCherry and the pigment-producing bacterium Streptomyces coelicolor A3(2)-mNeonGreen (mNG) growing on insoluble cellulose as a substrate. To validate the system, the fluorescence-to-biomass and fluorescence-to-scattered-light correlation of the two strains was characterized in depth under various conditions. Thereby, especially for complex filamentous microorganisms, microbial morphologies have to be considered. Another bias can arise from autofluorescence or pigments that can spectrally interfere with the fluorescence measurement. Green autofluorescence of both strains was uncoupled from different green fluorescent protein signals through a spectral unmixing approach, resulting in a specific signal only linked to the abundance of S. coelicolor A3(2)-mNG. As proof of principle, the population dynamics of the target co-culture were measured at varying inoculation ratios in presence of insoluble cellulose particles. Thereby, the respective fluorescence signals reliably described the abundance of each partner, according to the variations in the inocula. With this method, conditions can be fine-tuned for optimal growth of both partners along with natural product formation by the bacterium.

DnaK response to expression of protein mutants is dependent on translation rate and stability

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the λ-repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

Bacillus subtilis Histidine Kinase KinC Activates Biofilm Formation by Controlling Heterogeneity of Single-Cell Responses

In Bacillus subtilis, biofilm and sporulation pathways are both controlled by a master regulator, Spo0A, which is activated by phosphorylation via a phosphorelay-a cascade of phosphotransfer reactions commencing with autophosphorylation of histidine kinases KinA, KinB, KinC, KinD, and KinE. However, it is unclear how the kinases, despite acting via the same regulator, Spo0A, differentially regulate downstream pathways, i.e., how KinA mainly activates sporulation genes and KinC mainly activates biofilm genes. In this work, we found that KinC also downregulates sporulation genes, suggesting that KinC has a negative effect on Spo0A activity. To explain this effect, with a mathematical model of the phosphorelay, we revealed that unlike KinA, which always activates Spo0A, KinC has distinct effects on Spo0A at different growth stages: during fast growth, KinC acts as a phosphate source and activates Spo0A, whereas during slow growth, KinC becomes a phosphate sink and contributes to decreasing Spo0A activity. However, under these conditions, KinC can still increase the population-mean biofilm matrix production activity. In a population, individual cells grow at different rates, and KinC would increase the Spo0A activity in the fast-growing cells but reduce the Spo0A activity in the slow-growing cells. This mechanism reduces single-cell heterogeneity of Spo0A activity, thereby increasing the fraction of cells that activate biofilm matrix production. Thus, KinC activates biofilm formation by controlling the fraction of cells activating biofilm gene expression. IMPORTANCE In many bacterial and eukaryotic systems, multiple cell fate decisions are activated by a single master regulator. Typically, the activities of the regulators are controlled posttranslationally in response to different environmental stimuli. The mechanisms underlying the ability of these regulators to control multiple outcomes are not understood in many systems. By investigating the regulation of Bacillus subtilis master regulator Spo0A, we show that sensor kinases can use a novel mechanism to control cell fate decisions. By acting as a phosphate source or sink, kinases can interact with one another and provide accurate regulation of the phosphorylation level. Moreover, this mechanism affects the cell-to-cell heterogeneity of the transcription factor activity and eventually determines the fraction of different cell types in the population. These results demonstrate the importance of intercellular heterogeneity for understanding the effects of genetic perturbations on cell fate decisions. Such effects can be applicable to a wide range of cellular systems.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation-including static extrinsic noise-exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

Searching for principles of microbial physiology

Why do evolutionarily distinct microorganisms display similar physiological behaviours? Why are transitions from high-ATP yield to low(er)-ATP yield metabolisms so widespread across species? Why is fast growth generally accompanied with low stress tolerance? Do these regularities occur because most microbial species are subject to the same selective pressures and physicochemical constraints? If so, a broadly-applicable theory might be developed that predicts common microbiological behaviours. Microbial systems biologists have been working out the contours of this theory for the last two decades, guided by experimental data. At its foundations lie basic principles from evolutionary biology, enzyme biochemistry, metabolism, cell composition and steady-state growth. The theory makes predictions about fitness costs and benefits of protein expression, physicochemical constraints on cell growth and characteristics of optimal metabolisms that maximise growth rate. Comparisons of the theory with experimental data indicates that microorganisms often aim for maximisation of growth rate, also in the presence of stresses; they often express optimal metabolisms and metabolic proteins at optimal concentrations. This review explains the current status of the theory for microbiologists; its roots, predictions, experimental evidence and future directions.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

Reverse Engineering Targets for Recombinant Protein Production in Corynebacterium glutamicum Inspired by a Fast-Growing Evolved Descendant

We previously reported a Corynebacterium glutamicum JH41 strain with a 58% faster growth rate through application of adaptive laboratory evolution. To verify that the fast-reproducing strain was useful as a host for recombinant protein expression, we introduced a plasmid responsible for the secretory production of a recombinant protein. The JH41 strain harboring the plasmid indeed produced the secretory recombinant protein at a 2.7-fold greater rate than its ancestral strain. To provide the reverse engineering targets responsible for boosting recombinant protein production and cell reproduction, we compared the genome sequence of the JH41 strain with its ancestral strain. Among the 15 genomic variations, a point mutation was confirmed in the 14 bases upstream of NCgl1959 (encoding a presumed siderophore-binding protein). This mutation allowed derepression of NCgl1959, thereby increasing iron consumption and ATP generation. A point mutation in the structural gene ramA (A239G), a LuxR-type global transcription regulator involved in central metabolism, allowed an increase in glucose consumption. Therefore, mutations to increase the iron and carbon consumption were concluded as being responsible for the enhanced production of recombinant protein and cell reproduction in the evolved host.

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

Contributions to the 'noise floor' in gene expression in a population of dividing cells

Experiments with cells reveal the existence of a lower bound for protein noise, the noise floor, in highly expressed genes. Its origins are still debated. We propose a minimal model of gene expression in a proliferating bacterial cell population. The model predicts the existence of a noise floor and it semi-quantitatively reproduces the curved shape of the experimental noise vs. mean protein concentration plots. When the cell volume increases in a different manner than does the mean protein copy number, the noise floor level is determined by the cell population’s age structure and by the dependence of the mean protein concentration on cell age. Additionally, the noise floor level may depend on a biological limit for the mean number of bursts in the cell cycle. In that case, the noise floor level depends on the burst size distribution width but it is insensitive to the mean burst size. Our model quantifies the contributions of each of these mechanisms to gene expression noise.

Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied-particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

Exactly solvable model of gene expression in a proliferating bacterial cell population with stochastic protein bursts and protein partitioning

Many of the existing stochastic models of gene expression contain the first-order decay reaction term that may describe active protein degradation or dilution. If the model variable is interpreted as the molecule number, and not concentration, the decay term may also approximate the loss of protein molecules due to cell division as a continuous degradation process. The seminal model of that kind leads to gamma distributions of protein levels, whose parameters are defined by the mean frequency of protein bursts and mean burst size. However, such models (whether interpreted in terms of molecule numbers or concentrations) do not correctly account for the noise due to protein partitioning between daughter cells. We present an exactly solvable stochastic model of gene expression in cells dividing at random times, where we assume description in terms of molecule numbers with a constant mean protein burst size. The model is based on a population balance equation supplemented with protein production in random bursts. If protein molecules are partitioned equally between daughter cells, we obtain at steady state the analytical expressions for probability distributions similar in shape to gamma distributions, yet with quite different values of mean burst size and mean burst frequency than would result from fitting of the classical continuous-decay model to these distributions. For random partitioning of protein molecules between daughter cells, we obtain the moment equations for the protein number distribution and thus the analytical formulas for the squared coefficient of variation.

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

Ultrasensitive profiling of multiple biomarkers from single cells by signal amplification mass spectrometry

A signal amplification protocol based on mass spectrometry (MS) was developed to profile simultaneously multiple biomarkers from a single cell using various mass label (ML)-modified Au nanoparticles (AuNPs). The strategy with ultrahigh sensitivity and specificity has potential prospects in the deep exploration of molecular and cellular characterization.

Substrate and electron donor limitation induce phenotypic heterogeneity in different metabolic activities in a green sulphur bacterium

Populations of genetically identical cells can display marked variation in phenotypic traits; such variation is termed phenotypic heterogeneity. Here, we investigate the effect of substrate and electron donor limitation on phenotypic heterogeneity in N₂ and CO₂ fixation in the green sulphur bacterium Chlorobium phaeobacteroides. We grew populations in chemostats and batch cultures and used stable isotope labelling combined with nanometer-scale secondary ion mass spectrometry (NanoSIMS) to quantify phenotypic heterogeneity. Experiments in H₂ S (i.e. electron donor) limited chemostats show that varying levels of NH4+ limitation induce heterogeneity in N₂ fixation. Comparison of phenotypic heterogeneity between chemostats and batch (unlimited for H₂ S) populations indicates that electron donor limitation drives heterogeneity in N₂ and CO₂ fixation. Our results demonstrate that phenotypic heterogeneity in a certain metabolic activity can be driven by different modes of limitation and that heterogeneity can emerge in different metabolic processes upon the same mode of limitation. In conclusion, our data suggest that limitation is a general driver of phenotypic heterogeneity in microbial populations.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.