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Dissemination of food-borne L. monocytogenes in the host relies on internalinmediated invasion, but the underlying invasion strategies remain elusive. Here we use live-cell microscopy to follow single cell interactions between individual human cells and L. monocytogenes and elucidate mechanisms associated with internalin B (InlB)-mediated invasion. We demonstrate that whilst a replicative invasion of nonphagocytic cells is a rare event even at high multiplicities of invasion, L. monocytogenes overcomes this by utilising a strategy relaying on PrfA-mediated ActA-based aggregation. We show that L. monocytogenes forms aggregates in extracellular host cell environment, which promote approximately 5-fold more host cell adhesions than the non-aggregating actA-DC mutant (which lacks the C-terminus coding region), with the adhering bacteria inducing 3-fold more intracellular invasions. Aggregation is associated with robust MET tyrosine kinase receptor clustering in the host cells, a hallmark of InlB-mediated invasion, something not observed with the actA-DC mutant. Finally, we show via RNA-seq analyses that aggregation involves a global adaptive response to host cell environment (including iron depletion), resulting in metabolic changes in L. monocytogenes and upregulation of the PrfA virulence regulon. Overall, our analyses provide new mechanistic insights into internalin-mediated host-pathogen interactions of L. monocytogenes. [ABSTRACT FROM AUTHOR]

Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here, we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling and in culture mutation rate estimation, we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity (DAMP) when Escherichia coli populations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for DAMP, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition. In some bacteria, mutation rates depend on environmental factors such as population density. This study uses simulation-guided experiments to show that denser microbial populations detoxify their environment more efficiently, resulting in a reduced rate of mutation. [ABSTRACT FROM AUTHOR]

Pathogens have developed intricate strategies to overcome the host's innate immune responses. In this paper we use live-cell microscopy with a single bacterium resolution to follow in real time interactions between the food-borne pathogen L. monocytogenes and host macrophages, a key event controlling the infection in vivo. We demonstrate that infection results in heterogeneous outcomes, with only a subset of bacteria able to establish a replicative invasion of macrophages. The fate of individual bacteria in the same host cell was independent from the host cell and non-cooperative, being independent from co-infecting bacteria. A higher multiplicity of infection resulted in a reduced probability of replication of the overall bacterial population. By use of internalisation assays and conditional probabilities to mathematically describe the two-stage invasion process, we demonstrate that the higher MOI compromises the ability of macrophages to phagocytose bacteria. We found that the rate of phagocytosis is mediated via the secreted Listeriolysin toxin (LLO), while the probability of replication of intracellular bacteria remained constant. Using strains expressing fluorescent reporters to follow transcription of either the LLO-encoding hly or actA genes, we show that replicative bacteria exhibited higher PrfA regulon expression in comparison to those bacteria that did not replicate, however elevated PrfA expression per se was not sufficient to increase the probability of replication. Overall, this demonstrates a new role for the population-level, but not single cell, PrfA-mediated activity to regulate outcomes of host pathogen interactions. [ABSTRACT FROM AUTHOR]

The nuclear factor [kappa]B (NF-[kappa]B) transcription factor regulates cellular stress responses and the immune response to infection. NF-[kappa]B activation results in oscillations in nuclear NF-[kappa]B abundance. To define the function of these oscillations, we treated cells with repeated short pulses of tumor necrosis factor-[alpha] at various intervals to mimic pulsatile inflammatory signals. At all pulse intervals that were analyzed, we observed synchronous cycles of NF-[kappa]B nuclear translocation. Lower frequency stimulations gave repeated full-amplitude translocations, whereas higher frequency pulses gave reduced translocation, indicating a failure to reset. Deterministic and stochastic mathematical, models predicted how negative feedback loops regulate both the resetting of the system and cellular heterogeneity. Altering the stimulation intervals gave different patterns of NF-[kappa]B--dependent gene expression, which supports the idea that oscillation frequency has a functional role.

Abstract Background The NF-κB regulatory network controls innate immune response by transducing variety of pathogen-derived and cytokine stimuli into well defined single-cell gene regulatory events. Results We analyze the network by means of the model combining a deterministic description for molecular species with large cellular concentrations with two classes of stochastic switches: cell-surface receptor activation by TNFα ligand, and IκBα and A20 genes activation by NF-κB molecules. Both stochastic switches are associated with amplification pathways capable of translating single molecular events into tens of thousands of synthesized or degraded proteins. Here, we show that at a low TNFα dose only a fraction of cells are activated, but in these activated cells the amplification mechanisms assure that the amplitude of NF-κB nuclear translocation remains above a threshold. Similarly, the lower nuclear NF-κB concentration only reduces the probability of gene activation, but does not reduce gene expression of those responding. Conclusion These two effects provide a particular stochastic robustness in cell regulation, allowing cells to respond differently to the same stimuli, but causing their individual responses to be unequivocal. Both effects are likely to be crucial in the early immune response: Diversity in cell responses causes that the tissue defense is harder to overcome by relatively simple programs coded in viruses and other pathogens. The more focused single-cell responses help cells to choose their individual fates such as apoptosis or proliferation. The model supports the hypothesis that binding of single TNFα ligands is sufficient to induce massive NF-κB translocation and activation of NF-κB dependent genes.

The relationship between components of biochemical network and the resulting dynamics of the overall system is a key focus of computational biology. However, as these networks and resulting mathematical models are inherently complex and non-linear, the understanding of this relationship becomes challenging. Among many approaches, model reduction methods provide an avenue to extract components responsible for the key dynamical features of the system. Unfortunately, these approaches often require intuition to apply. In this manuscript we propose a practical algorithm for the reduction of biochemical reaction systems using fast-slow asymptotics. This method allows the ranking of system variables according to how quickly they approach their momentary steady state, thus selecting the fastest for a steady state approximation. We applied this method to derive models of the Nuclear Factor kappa B network, a key regulator of the immune response that exhibits oscillatory dynamics. Analyses with respect to two specific solutions, which corresponded to different experimental conditions identified different components of the system that were responsible for the respective dynamics. This is an important demonstration of how reduction methods that provide approximations around a specific steady state, could be utilised in order to gain a better understanding of network topology in a broader context. [ABSTRACT FROM AUTHOR]