Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels are vastly overabundant. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We show that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern central dogma processes, and these principles have broad implications for cellular function.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

The in vivo measurement of replication fork velocity and pausing by lag-time analysis

An important step towards understanding the mechanistic basis of the central dogma is the quantitative characterization of the dynamics of nucleic-acid-bound molecular motors in the context of the living cell. To capture these dynamics, we develop lag-time analysis, a method for measuring in vivo dynamics. Using this approach, we provide quantitative locus-specific measurements of fork velocity, in units of kilobases per second, as well as replisome pause durations, some with the precision of seconds. The measured fork velocity is observed to be both locus and time dependent, even in wild-type cells. In this work, we quantitatively characterize known phenomena, detect brief, locus-specific pauses at ribosomal DNA loci in wild-type cells, and observe temporal fork velocity oscillations in three highly-divergent bacterial species.

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

Genome-wide protein-DNA interaction site mapping in bacteria using a double-stranded DNA-specific cytosine deaminase

DNA-protein interactions are central to fundamental cellular processes, yet widely implemented technologies for measuring these interactions on a genome scale in bacteria are laborious and capture only a snapshot of binding events. We devised a facile method for mapping DNA-protein interaction sites in vivo using the double-stranded DNA-specific cytosine deaminase toxin DddA. In 3D-seq (DddA-sequencing), strains containing DddA fused to a DNA-binding protein of interest accumulate characteristic mutations in DNA sequence adjacent to sites occupied by the DNA-bound fusion protein. High-depth sequencing enables detection of sites of increased mutation frequency in these strains, yielding genome-wide maps of DNA-protein interaction sites. We validated 3D-seq for four transcription regulators in two bacterial species, Pseudomonas aeruginosa and Escherichia coli. We show that 3D-seq offers ease of implementation, the ability to record binding event signatures over time and the capacity for single-cell resolution.

Characterizing stochastic cell-cycle dynamics in exponential growth

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially growing population, while the other captures cell-cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: A single-cell deterministic description of cell-cycle dynamics where cell states (i.e., periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state ages in the deterministic model are equal to the exponential mean of the age in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell-cycle dynamics in other biological systems.

An interbacterial DNA deaminase toxin directly mutagenizes surviving target populations

When bacterial cells come in contact, antagonism mediated by the delivery of toxins frequently ensues. The potential for such encounters to have long-term beneficial consequences in recipient cells has not been investigated. Here, we examined the effects of intoxication by DddA, a cytosine deaminase delivered via the type VI secretion system (T6SS) of Burkholderia cenocepacia. Despite its killing potential, we observed that several bacterial species resist DddA and instead accumulate mutations. These mutations can lead to the acquisition of antibiotic resistance, indicating that even in the absence of killing, interbacterial antagonism can have profound consequences on target populations. Investigation of additional toxins from the deaminase superfamily revealed that mutagenic activity is a common feature of these proteins, including a representative we show targets single-stranded DNA and displays a markedly divergent structure. Our findings suggest that a surprising consequence of antagonistic interactions between bacteria could be the promotion of adaptation via the action of directly mutagenic toxins.

Correspondence between thermodynamics and inference

We expand upon a natural analogy between Bayesian statistics and statistical physics in which sample size corresponds to inverse temperature. This analogy motivates the definition of two statistical quantities: a learning capacity and a Gibbs entropy. The analysis of the learning capacity, corresponding to the heat capacity in thermal physics, leads to insight into the mechanism of learning and explains why some models have anomalously high learning performance. We explore the properties of the learning capacity in a number of examples, including a sloppy model. Next, we propose that the Gibbs entropy provides a natural device for counting distinguishable distributions in the context of Bayesian inference. We use this device to define a generalized principle of indifference in which every distinguishable model is assigned equal a priori probability. This principle results in a solution to a long-standing problem in Bayesian inference: the definition of an objective or uninformative prior. A key characteristic of this approach is that it can be applied to analyses where the model dimension is unknown and circumvents the automatic rejection of higher-dimensional models in Bayesian inference.

Bifunctional Immunity Proteins Protect Bacteria against FtsZ-Targeting ADP-Ribosylating Toxins

ADP-ribosylation of proteins can profoundly impact their function and serves as an effective mechanism by which bacterial toxins impair eukaryotic cell processes. Here, we report the discovery that bacteria also employ ADP-ribosylating toxins against each other during interspecies competition. We demonstrate that one such toxin from Serratia proteamaculans interrupts the division of competing cells by modifying the essential bacterial tubulin-like protein, FtsZ, adjacent to its protomer interface, blocking its capacity to polymerize. The structure of the toxin in complex with its immunity determinant revealed two distinct modes of inhibition: active site occlusion and enzymatic removal of ADP-ribose modifications. We show that each is sufficient to support toxin immunity; however, the latter additionally provides unprecedented broad protection against non-cognate ADP-ribosylating effectors. Our findings reveal how an interbacterial arms race has produced a unique solution for safeguarding the integrity of bacterial cell division machinery against inactivating post-translational modifications.

Strong disorder leads to scale invariance in complex biological systems

Despite the innate complexity of the cell, emergent scale-invariant behavior is observed in many biological systems. We investigate one example of this phenomenon: the dynamics of large complexes in the bacterial cytoplasm. The observed dynamics of these complexes is scale invariant in three measures of dynamics: mean-squared displacement (MSD), velocity autocorrelation function, and the step-size distribution. To investigate the physical mechanism for this emergent scale invariance, we explore minimal models in which mobility is modeled as diffusion on a rough free-energy landscape in one dimension. We discover that all three scale-invariant characteristics emerge generically in the strong disorder limit. (Strong disorder is defined by the divergence of the ensemble-averaged hop time between lattice sites.) In particular, we demonstrate how the scale invariance of the relative step-size distribution can be understood from the perspective of extreme-value theory in statistics (EVT). We show that the Gumbel scale parameter is simply related to the MSD scaling parameter. The EVT mechanism of scale invariance is expected to be generic to strongly disordered systems and therefore a powerful tool for the analysis of other systems in biology and beyond.

The bacterial replisome has factory-like localization

DNA replication is essential to cellular proliferation. The cellular-scale organization of the replication machinery (replisome) and the replicating chromosome has remained controversial. Two competing models describe the replication process: In the track model, the replisomes translocate along the DNA like a train on a track. Alternately, in the factory model, the replisomes form a stationary complex through which the DNA is pulled. We summarize the evidence for each model and discuss a number of confounding aspects that complicate interpretation of the observations. We advocate a factory-like model for bacterial replication where the replisomes form a relatively stationary and weakly associated complex that can transiently separate.

Noise in a phosphorelay drives stochastic entry into sporulation in Bacillus subtilis

Entry into sporulation in Bacillus subtilis is governed by a phosphorelay in which phosphoryl groups from a histidine kinase are successively transferred via relay proteins to the response regulator Spo0A. Spo0A~P, in turn, sets in motion events that lead to asymmetric division and activation of the cell-specific transcription factor σ^F, a hallmark for entry into sporulation. Here, we have used a microfluidics-based platform to investigate the activation of Spo0A and σ^F in individual cells held under constant, sporulation-inducing conditions. The principal conclusions were that: (i) activation of σ^F occurs with an approximately constant probability after adaptation to conditions of nutrient limitation; (ii) activation of σ^F is tightly correlated with, and preceded by, Spo0A~P reaching a high threshold level; (iii) activation of Spo0A takes place abruptly just prior to asymmetric division; and (iv) the primary source of noise in the activation of Spo0A is the phosphorelay. We propose that cells exhibit a constant probability of attaining a high threshold level of Spo0A~P due to fluctuations in the flux of phosphoryl groups through the phosphorelay.

Escherichia coli Chromosomal Loci Segregate from Midcell with Universal Dynamics

The structure of the Escherichia coli chromosome is inherently dynamic over the duration of the cell cycle. Genetic loci undergo both stochastic motion around their initial positions and directed motion to opposite poles of the rod-shaped cell during segregation. We developed a quantitative method to characterize cell-cycle dynamics of the E. coli chromosome to probe the chromosomal steady-state mobility and segregation process. By tracking fluorescently labeled chromosomal loci in thousands of cells throughout the entire cell cycle, our method allows for the statistical analysis of locus position and motion, the step-size distribution for movement during segregation, and the locus drift velocity. The robust statistics of our detailed analysis of the wild-type E. coli nucleoid allow us to observe loci moving toward midcell before segregation occurs, consistent with a replication factory model. Then, as segregation initiates, we perform a detailed characterization of the average segregation velocity of loci. Contrary to origin-centric models of segregation, which predict distinct dynamics for oriC-proximal versus oriC-distal loci, we find that the dynamics of loci were universal and independent of genetic position.

The Replisomes Remain Spatially Proximal throughout the Cell Cycle in Bacteria

The positioning of the DNA replication machinery (replisome) has been the subject of several studies. Two conflicting models for replisome localization have been proposed: In the Factory Model, sister replisomes remain spatially co-localized as the replicating DNA is translocated through a stationary replication factory. In the Track Model, sister replisomes translocate independently along a stationary DNA track and the replisomes are spatially separated for the majority of the cell cycle. Here, we used time-lapse imaging to observe and quantify the position of fluorescently labeled processivity-clamp (DnaN) complexes throughout the cell cycle in two highly-divergent bacterial model organisms: Bacillus subtilis and Escherichia coli. Because DnaN is a core component of the replication machinery, its localization patterns should be an appropriate proxy for replisome positioning in general. We present automated statistical analysis of DnaN positioning in large populations, which is essential due to the high degree of cell-to-cell variation. We find that both bacteria show remarkably similar DnaN positioning, where any potential separation of the two replication forks remains below the diffraction limit throughout the majority of the replication cycle. Additionally, the localization pattern of several other core replisome components is consistent with that of DnaN. These data altogether indicate that the two replication forks remain spatially co-localized and mostly function in close proximity throughout the replication cycle. The conservation of the observed localization patterns in these highly divergent species suggests that the subcellular positioning of the replisome is a functionally critical feature of DNA replication.

Cell proliferation depends on efficient replication of the genome. Bacteria typically have a single origin of replication on a circular chromosome. After replication initiation, two replisomes assemble at the origin and each copy one of the two arms of the chromosome until they reach the terminus. There have been conflicting reports about the subcellular positioning and putative co-localization of the two replication forks during this process. It has remained controversial whether the two replisomes remain relatively close to each other with the DNA being pulled through, or separate as they translocate along the DNA like a track. Existing studies have relied heavily on snapshot images and these experiments cannot unambiguously distinguish between these two models: i.e. two resolvable forks versus two pairs of co-localized forks. The ability of replication to re-initiate before cell division in bacterial cells further complicates the interpretation of these types of imaging studies. In this paper, we use a combination of snapshot imaging, time-lapse imaging, and quantitative analysis to measure the fraction of time forks are co-localized during each cell cycle. We find that the forks are co-localized for the majority (80%) of the replication cycle in two highly-divergent model organisms: B. subtilis and E. coli. Our observations are consistent with proximal localization of the two forks, but also some transient separations of sister forks during replication. The conserved behavior of sub-cellular positioning of the replisomes in these two highly divergent species implies a potential functional relevance of this feature.

Transcription leads to pervasive replisome instability in bacteria

The canonical model of DNA replication describes a highly-processive and largely continuous process by which the genome is duplicated. This continuous model is based upon in vitro reconstitution and in vivo ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells. Strikingly, the stoichiometries of the replicative helicase, DNA polymerase, and clamp loader complexes are consistent with the presence of only one active replisome in a significant fraction of cells (>40%). Furthermore, many of the observed complexes have short lifetimes (<8 min), suggesting that replisome disassembly is quite prevalent, possibly occurring several times per cell cycle. The instability of the replisome complex is conflict-induced: transcription inhibition stabilizes these complexes, restoring the second replisome in many of the cells. Our results suggest that, in contrast to the canonical model, DNA replication is a largely discontinuous process in vivo due to pervasive replication-transcription conflicts.

DOI:http://dx.doi.org/10.7554/eLife.19848.001

Transcription leads to pervasive replisome instability in bacteria

The canonical model of DNA replication describes a highly-processive and largely continuous process by which the genome is duplicated. This continuous model is based upon in vitro reconstitution and in vivo ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells. Strikingly, the stoichiometries of the replicative helicase, DNA polymerase, and clamp loader complexes are consistent with the presence of only one active replisome in a significant fraction of cells (>40%). Furthermore, many of the observed complexes have short lifetimes (<8 min), suggesting that replisome disassembly is quite prevalent, possibly occurring several times per cell cycle. The instability of the replisome complex is conflict-induced: transcription inhibition stabilizes these complexes, restoring the second replisome in many of the cells. Our results suggest that, in contrast to the canonical model, DNA replication is a largely discontinuous process in vivo due to pervasive replication-transcription conflicts.

Cytoplasmic RNA-Protein Particles Exhibit Non-Gaussian Subdiffusive Behavior

The cellular cytoplasm is a complex, heterogeneous environment (both spatially and temporally) that exhibits viscoelastic behavior. To further develop our quantitative insight into cellular transport, we analyze data sets of mRNA molecules fluorescently labeled with MS2-GFP tracked in real time in live Escherichia coli and Saccharomyces cerevisiae cells. As shown previously, these RNA-protein particles exhibit subdiffusive behavior that is viscoelastic in its origin. Examining the ensemble of particle displacements reveals a Laplace distribution at all observed timescales rather than the Gaussian distribution predicted by the central limit theorem. This ensemble non-Gaussian behavior is caused by a combination of an exponential distribution in the time-averaged diffusivities and non-Gaussian behavior of individual trajectories. We show that the non-Gaussian behavior is a consequence of significant heterogeneity between trajectories and dynamic heterogeneity along single trajectories. Informed by theory and simulation, our work provides an in-depth analysis of the complex diffusive behavior of RNA-protein particles in live cells.

Probing bacterial cell biology using image cytometry

Advances in automated fluorescence microscopy have made snapshot and time-lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high-throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population-level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate subpopulations) more than 70 cell descriptors, including cell size, age and fluorescence. The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame) and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.

An information-based approach to change-point analysis with applications to biophysics and cell biology

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data.

The Development of an Information Criterion for Change-Point Analysis

Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change point algorithm is used to identify the time indices (change points) at which the system transitions between these discrete states. We present a unified information-based approach to testing for the existence of change points. This new approach reconciles two previously disparate approaches to change-point analysis (frequentist and information based) for testing transitions between states. The resulting method is statistically principled, parameter and prior free, and widely applicable to a wide range of change-point problems.

Physical modeling of chromosome segregation in escherichia coli reveals impact of force and DNA relaxation

The physical mechanism by which Escherichia coli segregates copies of its chromosome for partitioning into daughter cells is unknown, partly due to the difficulty in interpreting the complex dynamic behavior during segregation. Analysis of previous chromosome segregation measurements in E. coli demonstrates that the origin of replication exhibits processive motion with a mean displacement that scales as t(0.32). In this work, we develop a model for segregation of chromosomal DNA as a Rouse polymer in a viscoelastic medium with a force applied to a single monomer. Our model demonstrates that the observed power-law scaling of the mean displacement and the behavior of the velocity autocorrelation function is captured by accounting for the relaxation of the polymer chain and the viscoelastic environment. We show that the ratio of the mean displacement to the variance of the displacement during segregation events is a critical metric that eliminates the compounding effects of polymer and medium dynamics and provides the segregation force. We calculate the force of oriC segregation in E. coli to be ∼0.49 pN.

Kin cell lysis is a danger signal that activates antibacterial pathways of Pseudomonas aeruginosa

The perception and response to cellular death is an important aspect of multicellular eukaryotic life. For example, damage-associated molecular patterns activate an inflammatory cascade that leads to removal of cellular debris and promotion of healing. We demonstrate that lysis of Pseudomonas aeruginosa cells triggers a program in the remaining population that confers fitness in interspecies co-culture. We find that this program, termed P. aeruginosa response to antagonism (PARA), involves rapid deployment of antibacterial factors and is mediated by the Gac/Rsm global regulatory pathway. Type VI secretion, and, unexpectedly, conjugative type IV secretion within competing bacteria, induce P. aeruginosa lysis and activate PARA, thus providing a mechanism for the enhanced capacity of P. aeruginosa to target bacteria that elaborate these factors. Our finding that bacteria sense damaged kin and respond via a widely distributed pathway to mount a complex response raises the possibility that danger sensing is an evolutionarily conserved process.

DOI:http://dx.doi.org/10.7554/eLife.05701.001

Bacteria live in diverse and changing environments where resources such as nutrients and space are often limited. They have thus evolved many survival strategies, including competitive and cooperative behaviors. In the first case, bacteria antagonize or prevent the growth of other microorganisms competing with them for resources, such as by generating antibiotics that specifically target rivals. During cooperation, bacteria may coordinate the production of compounds that have a shared benefit for members of their community.

In multicellular organisms, some cell types sense harmful microorganisms by the injury they cause in neighboring cells. This triggers a process that can lead to the production of molecules that kill the invaders and factors that promote the repair of cellular damage. An equivalent process has so far not been described for single-celled organisms such as bacteria. However, bacteria often live in structured groups containing many different species. In this type of growth environment, the ability of bacteria to sense when others of their species are attacked and to respond by taking measures to defend themselves could improve their chances of survival.

Now, LeRoux et al. reveal that the bacterium Pseudomonas aeruginosa is able to detect ‘danger signals’ released when neighboring P. aeruginosa cells are killed by other bacteria. These signals trigger a response in surviving cells by turning on a pathway that controls a number of antibacterial factors. These include the production of the so-called ‘type VI secretion system’, a molecular machine that delivers a potent cocktail of antibacterial toxins directly into nearby bacteria.

This process, which LeRoux et al. have named ‘P. aeruginosa response to antagonism’, or PARA for short, enables P. aeruginosa to thrive when grown with competing bacterial species. P. aeruginosa is notorious for infecting the lungs of people with the genetic disease cystic fibrosis, as well as chronic wounds often found in people with diabetes. In both cases, when P. aeruginosa is present, the numbers of other, often less harmful organisms, tend to decrease. PARA may be one reason for the success of P. aeruginosa in these multi-species infections.

DOI:http://dx.doi.org/10.7554/eLife.05701.002

Cytoplasmic dynamics reveals two modes of nucleoid-dependent mobility

It has been proposed that forces resulting from the physical exclusion of macromolecules from the bacterial nucleoid play a central role in organizing the bacterial cell, yet this proposal has not been quantitatively tested. To investigate this hypothesis, we mapped the generic motion of large protein complexes in the bacterial cytoplasm through quantitative analysis of thousands of complete cell-cycle trajectories of fluorescently tagged ectopic MS2-mRNA complexes. We find the motion of these complexes in the cytoplasm is strongly dependent on their spatial position along the long axis of the cell, and that their dynamics are consistent with a quantitative model that requires only nucleoid exclusion and membrane confinement. This analysis also reveals that the nucleoid increases the mobility of MS2-mRNA complexes, resulting in a fourfold increase in diffusion coefficients between regions of the lowest and highest nucleoid density. These data provide strong quantitative support for two modes of nucleoid action: the widely accepted mechanism of nucleoid exclusion in organizing the cell and a newly proposed mode, in which the nucleoid facilitates rapid motion throughout the cytoplasm.

Genome-scale quantitative characterization of bacterial protein localization dynamics throughout the cell cycle

Bacterial cells display both spatial and temporal organization, and this complex structure is known to play a central role in cellular function. Although nearly one-fifth of all proteins in Escherichia coli localize to specific subcellular locations, fundamental questions remain about how cellular-scale structure is encoded at the level of molecular-scale interactions. One significant limitation to our understanding is that the localization behavior of only a small subset of proteins has been characterized in detail. As an essential step toward a global model of protein localization in bacteria, we capture and quantitatively analyze spatial and temporal protein localization patterns throughout the cell cycle for nearly every protein in E. coli that exhibits nondiffuse localization. This genome-scale analysis reveals significant complexity in patterning, notably in the behavior of DNA-binding proteins. Complete cell-cycle imaging also facilitates analysis of protein partitioning to daughter cells at division, revealing a broad and robust assortment of asymmetric partitioning behaviors.

RNA mango aptamer-fluorophore: a bright, high-affinity complex for RNA labeling and tracking

Because RNA lacks strong intrinsic fluorescence, it has proven challenging to track RNA molecules in real time. To address this problem and to allow the purification of fluorescently tagged RNA complexes, we have selected a high affinity RNA aptamer called RNA Mango. This aptamer binds a series of thiazole orange (fluorophore) derivatives with nanomolar affinity, while increasing fluorophore fluorescence by up to 1,100-fold. Visualization of RNA Mango by single-molecule fluorescence microscopy, together with injection and imaging of RNA Mango/fluorophore complex in C. elegans gonads demonstrates the potential for live-cell RNA imaging with this system. By inserting RNA Mango into a stem loop of the bacterial 6S RNA and biotinylating the fluorophore, we demonstrate that the aptamer can be used to simultaneously fluorescently label and purify biologically important RNAs. The high affinity and fluorescent properties of RNA Mango are therefore expected to simplify the study of RNA complexes.

Unidirectional P-body transport during the yeast cell cycle

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained “hovering” near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions.

Estimating podocyte number and density using a single histologic section

The reduction in podocyte density to levels below a threshold value drives glomerulosclerosis and progression to ESRD. However, technical demands prohibit high-throughput application of conventional morphometry for estimating podocyte density. We evaluated a method for estimating podocyte density using single paraffin-embedded formalin-fixed sections. Podocyte nuclei were imaged using indirect immunofluorescence detection of antibodies against Wilms' tumor-1 or transducin-like enhancer of split 4. To account for the large size of podocyte nuclei in relation to section thickness, we derived a correction factor given by the equation CF=1/(D/T+1), where T is the tissue section thickness and D is the mean caliper diameter of podocyte nuclei. Normal values for D were directly measured in thick tissue sections and in 3- to 5-μm sections using calibrated imaging software. D values were larger for human podocyte nuclei than for rat or mouse nuclei (P<0.01). In addition, D did not vary significantly between human kidney biopsies at the time of transplantation, 3-6 months after transplantation, or with podocyte depletion associated with transplant glomerulopathy. In rat models, D values also did not vary with podocyte depletion, but increased approximately 10% with old age and in postnephrectomy kidney hypertrophy. A spreadsheet with embedded formulas was created to facilitate individualized podocyte density estimation upon input of measured values. The correction factor method was validated by comparison with other methods, and provided data comparable with prior data for normal human kidney transplant donors. This method for estimating podocyte density is applicable to high-throughput laboratory and clinical use.

c-di-GMP heterogeneity is generated by the chemotaxis machinery to regulate flagellar motility

Individual cell heterogeneity is commonly observed within populations, although its molecular basis is largely unknown. Previously, using FRET-based microscopy, we observed heterogeneity in cellular c-di-GMP levels. In this study, we show that c-di-GMP heterogeneity in Pseudomonas aeruginosa is promoted by a specific phosphodiesterase partitioned after cell division. We found that subcellular localization and reduction of c-di-GMP levels by this phosphodiesterase is dependent on the histidine kinase component of the chemotaxis machinery, CheA, and its phosphorylation state. Therefore, individual cell heterogeneity in c-di-GMP concentrations is regulated by the activity and the asymmetrical inheritance of the chemotaxis organelle after cell division. c-di-GMP heterogeneity results in a diversity of motility behaviors. The generation of diverse intracellular concentrations of c-di-GMP by asymmetric partitioning is likely important to the success and survival of bacterial populations within the environment by allowing a variety of motility behaviors.

DOI:http://dx.doi.org/10.7554/eLife.01402.001

Bacterial populations have traditionally been assumed to be made up of identical cells. However, while the bacteria within a population may be genetically identical, individual cells have different growth rates, metabolisms and motilities, among other things. This ‘phenotypic heterogeneity’ has been observed in many different species of bacteria, and in some cases it can be attributed to changes in the concentration of molecules called second messengers that help to relay signals from the external environment to targets within the cell.

It can be challenging to monitor changes in the concentration of specific molecules inside cells, but researchers recently developed a form of microscopy based on FRET (short for Forster resonance energy transfer) that can measure the levels of a second messenger molecule called cyclic di-guanylate (c-di-GMP) inside individual cells. This technique was used to study P. aeruginosa, a bacterium that has a single corkscrew-shaped propeller that enables it to swim through liquid. P. aeruginosa divides to form two daughter cells—one with a propeller and one without.

Although the daughter cell that does not have a propeller quickly grows one, FRET-based microscopy revealed that the daughter cell with a propeller had less c-di-GMP than the daughter without a propeller, but the reasons underlying this difference and its effects on bacterial behavior were not clear.

Now Kulasekara et al. show that the cell that inherits the propeller contains an enzyme that degrades c-di-GMP, and that the low levels of this second messenger molecule—caused by the enzyme being concentrated near the base of the propeller, and the presence of a protein (CheA) that enables the bacteria to swim towards sources of nutrients—result in faster swimming speeds and increased responsiveness to nutrients. In other words, although the two daughter cells are genetically identical, they behave quite differently because of the different levels of this second messenger molecule.

The existence of heterogeneity within a bacterial population likely leads to increased success and survival within changing diverse environments, and this work sets the stage for similar investigations into what establishes heterogeneity in other bacterial populations.

DOI:http://dx.doi.org/10.7554/eLife.01402.002

Mapping the driving forces of chromosome structure and segregation in Escherichia coli

The mechanism responsible for the accurate partitioning of newly replicated Escherichia coli chromosomes into daughter cells remains a mystery. In this article, we use automated cell cycle imaging to quantitatively analyse the cell cycle dynamics of the origin of replication (oriC) in hundreds of cells. We exploit the natural stochastic fluctuations of the chromosome structure to map both the spatial and temporal dependence of the motional bias segregating the chromosomes. The observed map is most consistent with force generation by an active mechanism, but one that generates much smaller forces than canonical molecular motors, including those driving eukaryotic chromosome segregation.

Diverse type VI secretion phospholipases are functionally plastic antibacterial effectors

Membranes allow the compartmentalization of biochemical processes and are therefore fundamental to life. The conservation of the cellular membrane, combined with its accessibility to secreted proteins, has made it a common target of factors mediating antagonistic interactions between diverse organisms. Here we report the discovery of a diverse superfamily of bacterial phospholipase enzymes. Within this superfamily, we defined enzymes with phospholipase A1 (PLA₁) and A2 (PLA₂) activity, which are common in host cell-targeting bacterial toxins and the venoms of certain insects and reptiles^1,2. However, we find that the fundamental role of the superfamily is to mediate antagonistic bacterial interactions as effectors of the type VI secretion system (T6SS) translocation apparatus; accordingly, we name these proteins type VI lipase effectors (Tle). Our analyses indicate that PldA of Pseudomonas aeruginosa, a eukaryotic-like phospholipase D (PLD)³, is a member of the Tle superfamily and the founding substrate of the haemolysin co-regulated protein secretion island II T6SS (H2-T6SS). While prior studies have specifically implicated PldA and the H2-T6SS in pathogenesis^3–5, we uncovered a specific role for the effector and its secretory machinery in intra- and inter-species bacterial interactions. Furthermore we find that this effector achieves its antibacterial activity by degrading phosphatidylethanolamine (PE), the major component of bacterial membranes. The surprising finding that virulence-associated phospholipases can serve as specific antibacterial effectors suggests that interbacterial interactions are a relevant factor driving the ongoing evolution of pathogenesis.

Surface sensing and lateral subcellular localization of WspA, the receptor in a chemosensory-like system leading to c-di-GMP production

Pseudomonas aeruginosa responds to growth on agar surfaces to produce cyclic-di-GMP, which stimulates biofilm formation. This is mediated by an alternative cellular function chemotaxis-like system called Wsp. The receptor protein WspA, is bioinformatically indistinguishable from methyl-accepting chemotaxis proteins. However, unlike standard chemoreceptors, WspA does not form stable clusters at cell poles. Rather, it forms dynamic clusters at both polar and lateral subcellular locations. To begin to study the mechanism of Wsp signal transduction in response to surfaces, we carried out a structure-function study of WspA and found that its C-terminus is important for its lateral subcellular localization and function. When this region was replaced with that of a chemoreceptor for amino acids, WspA became polarly localized. In addition, introduction of mutations in the C-terminal region of WspA that rendered this protein able to form more stable receptor-receptor interactions, also resulted in a WspA protein that was less capable of activating signal transduction. Receptor chimeras with a WspA C-terminus and N-terminal periplasmic domains from chemoreceptors that sense amino acids or malate responded to surfaces to produce c-di-GMP. Thus, the amino acid sequence of the WspA periplasmic region did not need to be conserved for the Wsp system to respond to surfaces.

Measuring chromosome conformation with degenerate labels

Although DNA conformation plays an integral role in all genetic processes from transcription to chromosome segregation, there is as yet no tractable method for capturing the in vivo conformation of a chromosome at high resolution. Labeling and fluorescently imaging thousands of loci along the chromosome would readily yield a conformation if each locus could be uniquely distinguished in the image, but this would unrealistically require thousands of distinguishable labels and a tedious experimental process. Here we present a computational method for extracting conformations when the total number of labels far exceeds the number of distinguishable labels. We evaluate our technique using simulated conformations with lengths ranging from 10 to 100 kilobases, and discuss the prospects for an experiment.

A model for Escherichia coli chromosome packaging supports transcription factor-induced DNA domain formation

What physical mechanism leads to organization of a highly condensed and confined circular chromosome? Computational modeling shows that confinement-induced organization is able to overcome the chromosome's propensity to mix by the formation of topological domains. The experimentally observed high precision of separate subcellular positioning of loci (located on different chromosomal domains) in Escherichia coli naturally emerges as a result of entropic demixing of such chromosomal loops. We propose one possible mechanism for organizing these domains: regulatory control defined by the underlying E. coli gene regulatory network requires the colocalization of transcription factor genes and target genes. Investigating this assumption, we find the DNA chain to self-organize into several topologically distinguishable domains where the interplay between the entropic repulsion of chromosomal loops and their compression due to the confining geometry induces an effective nucleoid filament-type of structure. Thus, we propose that the physical structure of the chromosome is a direct result of regulatory interactions. To reproduce the observed precise ordering of the chromosome, we estimate that the domain sizes are distributed between 10 and 700 kb, in agreement with the size of topological domains identified in the context of DNA supercoiling.

Protein-mediated molecular bridging: a key mechanism in biopolymer organization

Protein-mediated bridging is ubiquitous and essential for shaping cellular structures in all organisms. Here we dissect this mechanism for a model system: the Histone-like Nucleoid-Structuring protein (H-NS). We present data from two complementary single-molecule assays that probe the H-NS-DNA interaction: a dynamic optical-trap-driven unzipping assay and an equilibrium H-NS-mediated DNA looping scanning force microscopy imaging assay. To quantitatively analyze and compare these assays, we employ what we consider a novel theoretical framework that describes the bridging motif. The interplay between the experiments and our theoretical model not only infers the effective interaction free energy, the bridging conformation and the duplex-duplex spacing, but also reveals a second, unresolved, cis-binding mode that challenges our current understanding of the role of bridging proteins in chromatin structure. We expect that this theoretical framework for describing protein-mediated bridging will be applicable to proteins acting in chromatin and cytoskeletal organization.

Biological consequences of tightly bent DNA: the other life of a macromolecular celebrity

The mechanical properties of DNA play a critical role in many biological functions. For example, DNA packing in viruses involves confining the viral genome in a volume (the viral capsid) with dimensions that are comparable to the DNA persistence length. Similarly, eukaryotic DNA is packed in DNA-protein complexes (nucleosomes), in which DNA is tightly bent around protein spools. DNA is also tightly bent by many proteins that regulate transcription, resulting in a variation in gene expression that is amenable to quantitative analysis. In these cases, DNA loops are formed with lengths that are comparable to or smaller than the DNA persistence length. The aim of this review is to describe the physical forces associated with tightly bent DNA in all of these settings and to explore the biological consequences of such bending, as increasingly accessible by single-molecule techniques.