Infection by bacteriophage lambda: an evolving paradigm for cellular individuality

Coinfecting phages impede each other's entry into the cell

The developmental choice made by temperate phages, between cell death (lysis) and viral dormancy (lysogeny), is influenced by the relative abundance of viruses and hosts in the environment. The paradigm for this abundance-driven decision is phage lambda of E. coli, whose propensity to lysogenize increases with the number of viruses coinfecting the same bacterium. It is believed that lambda uses this number to infer whether phages or bacteria outnumber each other. However, this interpretation is premised on an accurate mapping between the extracellular phage-to-bacteria ratio and the intracellular multiplicity of infection (MOI). Here, we show this premise to be faulty. By simultaneously labeling phage capsids and genomes, we find that, while the number of phages landing on each cell reliably samples the population ratio, the number of phages entering the cell does not. Single-cell infections, performed in a microfluidic device and interpreted using a stochastic model, reveal that the probability and rate of phage entry decrease with the number of adsorbed phages. This decrease reflects an MOI-dependent perturbation to host physiology caused by phage attachment, as evidenced by compromised membrane integrity and loss of membrane potential. The dependence of entry dynamics on the surrounding medium results in a strong impact on the infection outcome, while the protracted entry of coinfecting phages increases the heterogeneity in infection outcome at a given MOI. Our findings in lambda, and similar results we obtained for phages T5 and P1, demonstrate the previously unappreciated role played by entry dynamics in determining the outcome of bacteriophage infection.

CO-INFECTING PHAGES IMPEDE EACH OTHER'S ENTRY INTO THE CELL

Bacteriophage lambda tunes its propensity to lysogenize based on the number of viral genome copies inside the infected cell. Viral self-counting is believed to serve as a way of inferring the abundance of available hosts in the environment. This interpretation is premised on an accurate mapping between the extracellular phage-to-bacteria ratio and the intracellular multiplicity of infection (MOI). However, here we show this premise to be untrue. By simultaneously labeling phage capsids and genomes, we find that, while the number of phages landing on each cell reliably samples the population ratio, the number of phages entering the cell does not. Single-cell infections, followed in a microfluidic device and interpreted using a stochastic model, reveal that the probability and rate of individual phage entries decrease with MOI. This decrease reflects an MOI-dependent perturbation to host physiology caused by phage landing, evidenced by compromised membrane integrity and loss of membrane potential. The dependence of phage entry dynamics on the surrounding medium is found to result in a strong impact of environmental conditions on the infection outcome, while the protracted entry of co-infecting phages increases the cell-to-cell variability in infection outcome at a given MOI. Our findings demonstrate the previously unappreciated role played by entry dynamics in determining the outcome of bacteriophage infection.

Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions

Genetically identical bacterial cells commonly display different phenotypes. This phenotypic heterogeneity is well known for stress responses, where it is often explained as bet hedging against unpredictable environmental threats. Here, we explore phenotypic heterogeneity in a major stress response of Escherichia coli and find it has a fundamentally different basis. We characterize the response of cells exposed to hydrogen peroxide (H2O2) stress in a microfluidic device under constant growth conditions. A machine-learning model reveals that phenotypic heterogeneity arises from a precise and rapid feedback between each cell and its immediate environment. Moreover, we find that the heterogeneity rests upon cell-cell interaction, whereby cells shield each other from H2O2 via their individual stress responses. Our work shows how phenotypic heterogeneity in bacterial stress responses can emerge from short-range cell-cell interactions and result in a collective phenotype that protects a large proportion of the population.

Negative DNA supercoiling makes protein-mediated looping deterministic and ergodic within the bacterial doubling time

Protein-mediated DNA looping is fundamental to gene regulation and such loops occur stochastically in purified systems. Additional proteins increase the probability of looping, but these probabilities maintain a broad distribution. For example, the probability of lac repressor-mediated looping in individual molecules ranged 0–100%, and individual molecules exhibited representative behavior only in observations lasting an hour or more. Titrating with HU protein progressively compacted the DNA without narrowing the 0–100% distribution. Increased negative supercoiling produced an ensemble of molecules in which all individual molecules more closely resembled the average. Furthermore, in only 12 min of observation, well within the doubling time of the bacterium, most molecules exhibited the looping probability of the ensemble. DNA supercoiling, an inherent feature of all genomes, appears to impose time-constrained, emergent behavior on otherwise random molecular activity.

Bacteriophage-Liposomes Complex, a Bi-therapy System to Target Streptococcus pneumonia and Biofilm: A Research Protocol

Introduction: Streptococcus pneumoniae is a gram-positive bacterium, which is the leading cause of death for young children, elderly population, and immunocompromised patients. Its ability to mutate and become resistant to some of the strongest antibiotics makes them difficult to treat and increases the risk of disease spread. Although the development of stronger antibiotics to treat such microbes may be an option, they potentially pose a dangerous threat to the body. As such, a viable treatment option to fight against antimicrobial resistance has yet been found. Methods: The study focuses on utilizing a bi-therapy system to target S. pneumoniae in biofilm, which is the site of emerging antibiotic resistant mutants, by creating levofloxacin-liposomes carrying phages and testing them both in vitro and in vivo. Anticipated results: Using bacteriophage therapy and applying bacteriophage-antibiotic synergy, it is hoped to augment the potency of the treatment while lowering its side-effects. The Cp-1 bacteriophage-liposomes complexes are expected to be specific to the S. pneumoniae to carry antibiotics to sites of infection. Discussion: The therapy could ensure targeted bacterial lysis and site-directed delivery of low-dose drugs to decrease the toxicity effect of the antibiotics. Once the efficacy is established and is proven to be significant, its potency can be tested in BALB/cByJ mice models before bringing this therapy to animal trials then human clinical trials. Conclusion: Bacteriophages are very attractive therapeutic agents that effectively target pathogenic bacteria, safe for the human body, and highly modifiable to combat newly emerging bacterial threats. In addition to its many benefits, the use of bacteriophages could significantly reduce healthcare costs. The potential use of bacteriophages-liposomes complexes could be translated to treat respiratory infections in humans after confirming its efficacy in vitro and in vivo studies.

Analysis of Infection Time Courses Shows CII Levels Determine the Frequency of Lysogeny in Phage 186

Engineered phage with properties optimised for the treatment of bacterial infections hold great promise, but require careful characterisation by a number of approaches. Phage–bacteria infection time courses, where populations of bacteriophage and bacteria are mixed and followed over many infection cycles, can be used to deduce properties of phage infection at the individual cell level. Here, we apply this approach to analysis of infection of Escherichia coli by the temperate bacteriophage 186 and explore which properties of the infection process can be reliably inferred. By applying established modelling methods to such data, we extract the frequency at which phage 186 chooses the lysogenic pathway after infection, and show that lysogenisation increases in a graded manner with increased expression of the lysogenic establishment factor CII. The data also suggest that, like phage λ, the rate of lysogeny of phage 186 increases with multiple infections.

Protein hourglass: Exact first passage time distributions for protein thresholds

Protein thresholds have been shown to act as an ancient timekeeping device, such as in the time to lysis of Escherichia coli infected with bacteriophage λ. The time taken for protein levels to reach a particular threshold for the first time is defined as the first passage time (FPT) of the protein synthesis system, which is a stochastic quantity. The first few moments of the distribution of first passage times were known earlier, but an analytical expression for the full distribution was not available. In this work, we derive an analytical expression for the first passage times for a long-lived protein. This expression allows us to calculate the full distribution not only for cases of no self-regulation, but also for both positive and negative self-regulation of the threshold protein. We show that the shape of the distribution matches previous experimental data on λ-phage lysis time distributions. We also provide analytical expressions for the FPT distribution with non-zero degradation in Laplace space. Furthermore, we study the noise in the precision of the first passage times described by coefficient of variation (CV) of the distribution as a function of the protein threshold value. We show that under conditions of positive self-regulation, the CV declines monotonically with increasing protein threshold, while under conditions of linear negative self-regulation, there is an optimal protein threshold that minimizes the noise in the first passage times.

A molecular mechanism for probabilistic bet hedging and its role in viral latency

Probabilistic bet hedging is a generalized diversification strategy to maximize fitness in unpredictable environments and has been proposed as an evolutionary basis for herpesvirus latency. However, the molecular mechanisms enabling probabilistic bet hedging have remained elusive. Here, we find that the human herpesvirus cytomegalovirus—a major cause of birth defects and transplant failures—utilizes stochastic variability in the abundance of a protein packaged into individual viral particles to enable probabilistic bet hedging between alternate viral states.

Probabilistic bet hedging, a strategy to maximize fitness in unpredictable environments by matching phenotypic variability to environmental variability, is theorized to account for the evolution of various fate-specification decisions, including viral latency. However, the molecular mechanisms underlying bet hedging remain unclear. Here, we report that large variability in protein abundance within individual herpesvirus virion particles enables probabilistic bet hedging between viral replication and latency. Superresolution imaging of individual virions of the human herpesvirus cytomegalovirus (CMV) showed that virion-to-virion levels of pp71 tegument protein—the major viral transactivator protein—exhibit extreme variability. This super-Poissonian tegument variability promoted alternate replicative strategies: high virion pp71 levels enhance viral replicative fitness but, strikingly, impede silencing, whereas low virion pp71 levels reduce fitness but promote silencing. Overall, the results indicate that stochastic tegument packaging provides a mechanism enabling probabilistic bet hedging between viral replication and latency.

When to be temperate: on the fitness benefits of lysis vs. lysogeny

Bacterial viruses, that is ‘bacteriophage’ or ‘phage’, can infect and lyse their bacterial hosts, releasing new viral progeny. In addition to the lytic pathway, certain bacteriophage (i.e. ‘temperate’ bacteriophage) can also initiate lysogeny, a latent mode of infection in which the viral genome is integrated into and replicated with the bacterial chromosome. Subsequently, the integrated viral genome, that is the ‘prophage’, can induce and restart the lytic pathway. Here, we explore the relationship among infection mode, ecological context, and viral fitness, in essence asking: when should viruses be temperate? To do so, we use network loop analysis to quantify fitness in terms of network paths through the life history of an infectious pathogen that start and end with infected cells. This analysis reveals that temperate strategies, particularly those with direct benefits to cellular fitness, should be favored at low host abundances. This finding applies to a spectrum of mechanistic models of phage–bacteria dynamics spanning both explicit and implicit representations of intra-cellular infection dynamics. However, the same analysis reveals that temperate strategies, in and of themselves, do not provide an advantage when infection imposes a cost to cellular fitness. Hence, we use evolutionary invasion analysis to explore when temperate phage can invade microbial communities with circulating lytic phage. We find that lytic phage can drive down niche competition amongst microbial cells, facilitating the subsequent invasion of latent strategies that increase cellular resistance and/or immunity to infection by lytic viruses—notably this finding holds even when the prophage comes at a direct fitness cost to cellular reproduction. Altogether, our analysis identifies broad ecological conditions that favor latency and provide a principled framework for exploring the impacts of ecological context on both the short- and long-term benefits of being temperate.

Altered Growth and Envelope Properties of Polylysogens Containing Bacteriophage Lambda N-cI- Prophages

The bacterial virus lambda (λ) is a temperate bacteriophage that can lysogenize host Escherichia coli (E. coli) cells. Lysogeny requires λ repressor, the cI gene product, which shuts off transcription of the phage genome. The λ N protein, in contrast, is a transcriptional antiterminator, required for expression of the terminator-distal genes, and thus, λ N mutants are growth-defective. When E. coli is infected with a λ double mutant that is defective in both N and cI (i.e., λN^-cI^-), at high multiplicities of 50 or more, it forms polylysogens that contain 20–30 copies of the λN^-cI^- genome integrated in the E. coli chromosome. Early studies revealed that the polylysogens underwent “conversion” to long filamentous cells that form tiny colonies on agar. Here, we report a large set of altered biochemical properties associated with this conversion, documenting an overall degeneration of the bacterial envelope. These properties reverted back to those of nonlysogenic E. coli as the metastable polylysogen spontaneously lost the λN^-cI^- genomes, suggesting that conversion is a direct result of the multiple copies of the prophage. Preliminary attempts to identify lambda genes that may be responsible for conversion ruled out several candidates, implicating a potentially novel lambda function that awaits further studies.

Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality

Single-cell measurements of mRNA copy-number inform our understanding of stochastic gene expression [1-3], but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation stochastically take plasce [4, 5]. Here we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. Interpreted using a theoretical model for mRNA dynamics, the single-cell data allows us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity, and instead highlight the contribution of previously hidden variables to the observed population heterogeneity.

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.

Single-Molecule Kinetics in Living Cells

In the past decades, advances in microscopy have made it possible to study the dynamics of individual biomolecules in vitro and resolve intramolecular kinetics that would otherwise be hidden in ensemble averages. More recently, single-molecule methods have been used to image, localize, and track individually labeled macromolecules in the cytoplasm of living cells, allowing investigations of intermolecular kinetics under physiologically relevant conditions. In this review, we illuminate the particular advantages of single-molecule techniques when studying kinetics in living cells and discuss solutions to specific challenges associated with these methods.