ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are ...

The Hebrew University of Jerusalem

The conversion of physical signals, such as contractile forces or external mechanical perturbations, into chemical signaling events is a fundamental cellular process that occurs at cell-extracellular matrix contacts, known as focal adhesions. At these sites, transmembrane integrin receptors are associated via their cytoplasmic domains with the actin cytoskeleton. This interaction with actin is mediated by a submembrane plaque, consisting of numerous cytoskeletal and signaling molecules. Application of intrinsic or external forces to these structures dramatically affects their assembly and triggers adhesion-mediated signaling. In this review, we discuss the structure-function relationships of focal adhesions and the possible mode of action of the putative mechanosensor associated with them. We also discuss the general phenomenon of mechanosensitivity, and the approaches used to measure local forces at adhesion sites, the cytoskeleton-mediated regulation of local contractility, and the nature of the signaling networks that both affect contractility and are affected by it.

Adhesion-dependent cell mechanosensitivity.

A fraction of a genetically homogeneous microbial population may survive exposure to stress such as antibiotic treatment. Unlike resistant mutants, cells regrown from such persistent bacteria remain sensitive to the antibiotic. We investigated the persistence of single cells of Escherichia coli with the use of microfluidic devices. Persistence was linked to preexisting heterogeneity in bacterial populations because phenotypic switching occurred between normally growing cells and persister cells having reduced growth rates. Quantitative measurements led to a simple mathematical description of the persistence switch. Inherent heterogeneity of bacterial populations may be important in adaptation to fluctuating environments and in the persistence of bacterial infections.

Bacterial persistence as a phenotypic switch.

The persistence phenotype is an epigenetic trait exhibited by a subpopulation of bacteria, characterized by slow growth coupled with an ability to survive antibiotic treatment. The phenotype is acquired via a spontaneous, reversible switch between normal and persister cells. These observations suggest that clonal bacterial populations may use persister cells, whose slow division rate under growth conditions leads to lower population fitness, as an "insurance policy" against antibiotic encounters. We present a model of Escherichia coli persistence, and using experimentally derived parameters for both wild type and a mutant strain (hipQ) with markedly different switching rates, we show how fitness loss due to slow persister growth pays off as a risk-reducing strategy. We demonstrate that wild-type persistence is suited for environments in which antibiotic stress is a rare event. The optimal rate of switching between normal and persister cells is found to depend strongly on the frequency of environmental changes and only weakly on the selective pressures of any given environment. In contrast to typical examples of adaptations to features of a single environment, persistence appears to constitute an adaptation that is tuned to the distribution of environmental change.

Bacterial persistence: a model of survival in changing environments.

We discuss recent developments in the modeling of negative autoregulated genetic networks. In particular, we consider the temporal evolution of the population of mRNA and proteins in simple networks using rate equations. In the limit of low copy numbers, fluctuation effects become significant and more adequate modeling is then achieved using the master equation formalism. The analogy between regulatory gene networks and chemical reaction networks on dust grains in the interstellar medium is discussed. The analysis and simulation of complex reaction networks are also considered.

Modeling of negative autoregulated genetic networks in single cells.

Szilard's dream.

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic and stochastic methods. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. Here we show that, for a range of biologically relevant conditions, a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding.

Genetic toggle switch without cooperative binding.

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic methods (rate equations) and stochastic methods (the master equation and Monte Carlo simulations). These systems exhibit bistability, namely two stable states such that spontaneous transitions between them are rare. Induced transitions may take place as a result of an external stimulus. We study several variants of the genetic switch and examine the effects of cooperative binding, exclusive binding, protein-protein interactions, and degradation of bound repressors. We identify the range of parameters in which bistability takes place, enabling the system to function as a switch. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. We show that a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding. The average time between spontaneous transitions is evaluated as a function of the biological parameters.

Stochastic simulations of genetic switch systems.

Phenotypic variability in populations of cells has been linked to evolutionary robustness to stressful conditions. A remarkable example of the importance of cell-to-cell variability is found in bacterial persistence, where subpopulations of dormant bacteria, termed persisters, were shown to be responsible for the persistence of the population to antibiotic treatments. Here, we use microfluidic devices to monitor the induction of fluorescent proteins under synthetic promoters and characterize the dormant state of single persister bacteria. Surprisingly, we observe that protein production does take place in supposedly dormant bacteria, over a narrow time window after the exit from stationary phase. Only thereafter does protein production stop, suggesting that differentiation into persisters fully develops over this time window and not during starvation, as previously believed. In effect, we observe that exposure of bacteria to antibiotics during this time window significantly reduces persistence. Our results point to new strategies to fight persistent bacterial infections. The quantitative measurement of single-cell induction presented in this study should shed light on the processes leading to the dormancy of subpopulations in different systems, such as in subpopulations of viable but nonculturable bacteria, or those of quiescent cancer cells.

Single-cell protein induction dynamics reveals a period of vulnerability to antibiotics in persister bacteria.

A recent publication has reported the fabrication of a new microbial culture device containing an array of unprecedented density - a million growth chambers. This micro-Petri chip should impact on various fields, including biotechnology, ecology, food microbiology and drug development, by enabling high-throughput screens and therefore the detection of rare phenotypes within a large population of microorganisms.

The Moore's Law of microbiology - towards bacterial culture miniaturization with the micro-Petri chip.

Isogenic bacteria can exhibit a range of phenotypes, even in homogeneous environmental conditions. Such nongenetic individuality has been observed in a wide range of biological processes, including differentiation and stress response. A striking example is the heterogeneous response of bacteria to antibiotics, whereby a small fraction of drug-sensitive bacteria can persist under extensive antibiotic treatments. We have previously shown that persistent bacteria enter a phenotypic state, identified by slow growth or dormancy, which protects them from the lethal action of antibiotics. Here, we studied the effect of persistence on the interaction between Escherichia coli and phage lambda. We used long-term time-lapse microscopy to follow the expression of green fluorescent protein (GFP) under the phage lytic promoter, as well as cellular fate, in single infected bacteria. Intriguingly, we found that, whereas persistent bacteria are protected from prophage induction, they are not protected from lytic infection. Quantitative analysis of gene expression reveals that the expression of lytic genes is suppressed in persistent bacteria. However, when persistent bacteria switch to normal growth, the infecting phage resumes the process of gene expression, ultimately causing cell lysis. Using mathematical models for these two host-phage interactions, we found that the bacteria's nongenetic individuality can significantly affect the population dynamics, and might be relevant for understanding the coevolution of bacterial hosts and phages.

Nongenetic individuality in the host-phage interaction.

While the DNA sequence is largely responsible for transmitting phenotypic traits over evolutionary time, organisms are also considerably affected by phenotypic variations that persist for more than one generation, with no direct change in the organisms' DNA sequence. In contrast to genetic variation, which is passed on over many generations, the phenotypic variation generated by nongenetic mechanisms is difficult to study due to the inherently limited life time of states that are not encoded in the DNA sequence, but makes it possible for the 'memory' of past environments to influence future organisms. One striking example of phenotypic variation is the phenomenon of bacterial persistence, whereby genetically identical bacterial populations respond heterogeneously to antibiotic treatment. Our aim is to review several experimental and theoretical approaches to the study of persistence. We define persistence as a characteristic of a heterogeneous bacterial population that is taken as a generic example through which we illustrate the approach and study the dynamics of population variability. The clinical and evolutionary implications of persistence are discussed in light of the mathematical description. This approach should be of relevance to the study of other phenomena in which nongenetic variability is involved, such as cellular differentiation or the response of cancer cells to treatment.

The importance of being persistent: heterogeneity of bacterial populations under antibiotic stress.

In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed "persistence," is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin-antitoxin module implicated in antibiotic persistence of Escherichia coli. We find that bacterial cells become dormant if the toxin level is higher than a threshold, and that the amount by which the threshold is exceeded determines the duration of dormancy. Fluctuations in toxin levels above and below the threshold result in coexistence of dormant and growing cells. We conclude that toxin-antitoxin modules in general represent a mixed network motif that can serve to produce a subpopulation of dormant cells and to supply a mechanism for regulating the frequency and duration of growth arrest. Toxin-antitoxin modules thus provide a natural molecular design for implementing a bet-hedging strategy.

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

We developed an automated system, ScanLag, that measures in parallel the delay in growth (lag time) and growth rate of thousands of cells. Using ScanLag, we detected small subpopulations of bacteria with dramatically increased lag time upon starvation. By screening a library of Escherichia coli deletion mutants, we achieved two-dimensional mapping of growth characteristics, which showed that ScanLag enables multidimensional screens for quantitative characterization and identification of rare phenotypic variants.

Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes.

Using a device termed the 'morbidostat', a recent study sheds new light on the determinism of genetic and phenotypic trajectories leading to high antibiotic resistance.

Whack-an-E. coli with the morbidostat.

It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.

Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

The current antibiotic resistance crisis has led to increased pressure to prioritize strategies to tackle the issue, with a strong focus being placed on the development of novel antimicrobials. However, one major obstacle that is often overlooked is persister cells, which are refractory to antibiotic treatment. Tackling persistence is a challenge because these cell types are extremely difficult to study and, consequently, little is known about their physiology and the factors that lead to their emergence. Here, four experts contemplate the main physiological features that define persistence and the implications of persistence for antibiotic treatment regimens, and consider what the study of bacterial persistence has taught us about the heterogeneity of bacterial populations.

A problem of persistence: still more questions than answers?

Bacterial persistence has been shown to be an underlying factor in the failure of antibiotic treatments. Although many pathways, among them the stringent response and toxin-antitoxin modules, have been linked to antibiotic persistence, a clear molecular mechanism for the growth arrest that characterizes persistent bacteria remained elusive. Here, we screened an expression library for putative targets of HipA, the first toxin linked to persistence, and a serine/threonine kinase. We found that the expression of GltX, the glutamyl-tRNA-synthetase, reverses the toxicity of HipA and prevents persister formation. We show that upon HipA expression, GltX undergoes phosphorylation at Ser239, its ATP-binding site. This phosphorylation leads to accumulation of uncharged tRNA(Glu) in the cell, which results in the activation of the stringent response. Our findings demonstrate a mechanism for persister formation by the hipBA toxin-antitoxin module and provide an explanation for the long-observed connection between persistence and the stringent response.

HipA-mediated antibiotic persistence via phosphorylation of the glutamyl-tRNA-synthetase.

Exponentially growing bacteria are rarely found in the wild, as microorganisms tend to spend most of their lifetime at stationary phase. Despite this general prevalence of stationary-phase bacteria, they are as yet poorly characterized. Our goal was to quantitatively study this phase by direct observation of single bacteria as they enter into stationary phase and by monitoring their activity over several days during growth arrest. For this purpose, we devised an experimental procedure for starving single Escherichia coli bacteria in microfluidic devices and measured their activity by monitoring the production rate of fluorescent proteins. When amino acids were the sole carbon source, the production rate decreased by an order of magnitude upon entry into stationary phase. We found that, even while growth-arrested, bacteria continued to produce proteins at a surprisingly constant rate over several days. Our identification of this newly observed period of constant activity in nongrowing cells, designated as constant activity stationary phase, makes possible the conduction of assays that require constant protein expression over time, and are therefore difficult to perform under exponential growth conditions. Moreover, we show that exogenous protein expression bears no fitness cost on the regrowth of the population when starvation ends. Further characterization of constant activity stationary phase-a phase where nongrowing bacteria can be quantitatively studied over several days in a reproducible manner-should contribute to a better understanding of this ubiquitous but overlooked physiological state of bacteria in nature.

Nathalie Q. Balaban

Racah Institute of Physics

The Hebrew University of Jerusalem

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Nathalie Q. Balaban

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The Hebrew University of Jerusalem

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Racah Institute of Physics