Browsing by Author "Lipniacki, Tomasz"

Now showing 1 - 5 of 5
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    Antagonism between viral infection and innate immunity at the single-cell level
    (Public Library of Science, 2023) Grabowski, Frederic; Kochańczyk, Marek; Korwek, Zbigniew; Czerkies, Maciej; Prus, Wiktor; Lipniacki, Tomasz
    When infected with a virus, cells may secrete interferons (IFNs) that prompt nearby cells to prepare for upcoming infection. Reciprocally, viral proteins often interfere with IFN synthesis and IFN-induced signaling. We modeled the crosstalk between the propagating virus and the innate immune response using an agent-based stochastic approach. By analyzing immunofluorescence microscopy images we observed that the mutual antagonism between the respiratory syncytial virus (RSV) and infected A549 cells leads to dichotomous responses at the single-cell level and complex spatial patterns of cell signaling states. Our analysis indicates that RSV blocks innate responses at three levels: by inhibition of IRF3 activation, inhibition of IFN synthesis, and inhibition of STAT1/2 activation. In turn, proteins coded by IFN-stimulated (STAT1/2-activated) genes inhibit the synthesis of viral RNA and viral proteins. The striking consequence of these inhibitions is a lack of coincidence of viral proteins and IFN expression within single cells. The model enables investigation of the impact of immunostimulatory defective viral particles and signaling network perturbations that could potentially facilitate containment or clearance of the viral infection.
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    Cell fate in antiviral response arises in the crosstalk of IRF, NF-κB and JAK/STAT pathways
    (Springer Nature, 2018) Czerkies, Maciej; Korwek, Zbigniew; Prus, Wiktor; Kochańczyk, Marek; Jaruszewicz-Błońska, Joanna; Tudelska, Karolina; Błoński, Sławomir; Kimmel, Marek; Brasier, Allan R.; Lipniacki, Tomasz; Bioengineering; Statistics
    The innate immune system processes pathogen-induced signals into cell fate decisions. How information is turned to decision remains unknown. By combining stochastic mathematical modelling and experimentation, we demonstrate that feedback interactions between the IRF3, NF-κB and STAT pathways lead to switch-like responses to a viral analogue, poly(I:C), in contrast to pulse-like responses to bacterial LPS. Poly(I:C) activates both IRF3 and NF-κB, a requirement for induction of IFNβ expression. Autocrine IFNβ initiates a JAK/STAT-mediated positive-feedback stabilising nuclear IRF3 and NF-κB in first responder cells. Paracrine IFNβ, in turn, sensitises second responder cells through a JAK/STAT-mediated positive feedforward pathway that upregulates the positive-feedback components: RIG-I, PKR and OAS1A. In these sensitised cells, the ‘live-or-die’ decision phase following poly(I:C) exposure is shorter—they rapidly produce antiviral responses and commit to apoptosis. The interlinked positive feedback and feedforward signalling is key for coordinating cell fate decisions in cellular populations restricting pathogen spread.
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    Dynamic Cross Talk Model of the Epithelial Innate Immune Response to Double-Stranded RNA Stimulation: Coordinated Dynamics Emerging from Cell-Level Noise
    (Public Library of Science, 2014) Bertolusso, Roberto; Tian, Bing; Zhao, Yingxin; Vergara, Leoncio; Sabree, Aqeeb; Iwanaszko, Marta; Lipniacki, Tomasz; Brasier, Allan R.; Kimmel, Marek
    We present an integrated dynamical cross-talk model of the epithelial innate immune reponse (IIR) incorporating RIG-I and TLR3 as the two major pattern recognition receptors (PRR) converging on the RelA and IRF3 transcriptional effectors. bioPN simulations reproduce biologically relevant gene-and protein abundance measurements in response to time course, gene silencing and dose-response perturbations both at the population and single cell level. Our computational predictions suggest that RelA and IRF3 are under auto- and cross-regulation. We predict, and confirm experimentally, that RIG-I mRNA expression is controlled by IRF7. We also predict the existence of a TLR3-dependent, IRF3-independent transcription factor (or factors) that control(s) expression of MAVS, IRF3 and members of the IKK family. Our model confirms the observed dsRNA dose-dependence of oscillatory patterns in single cells, with periods of 1-3 hr. Model fitting to time series, matched by knockdown data suggests that the NF-kB module operates in a different regime (with different coefficient values) than in the TNFa-stimulation experiments. In future studies, this model will serve as a foundation for identification of virus-encoded IIR antagonists and examination of stochastic effects of viral replication. Our model generates simulated time series, which reproduce the noisy oscillatory patterns of activity (with 1-3 hour period) observed in individual cells. Our work supports the hypothesis that the IIR is a phenomenon that emerged by evolution despite highly variable responses at an individual cell level.
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    Modeling stochasticity in gene regulation
    (2006) Paszek, Pawel; Kimmel, Marek; Lipniacki, Tomasz
    Intrinsic stochasticity plays an essential role in gene regulation because of the small number of involved molecules of DNA, mRNA and protein of a given species. To better understand this phenomenon, small gene regulatory systems are mathematically modeled as systems of coupled chemical reactions, but the existing exact description utilizing a Chapman-Kolmogorov equation or simulation algorithms is limited and inefficient. The present work introduces a much more efficient yet accurate modeling approach, which allows analyzing stochasticity in the system in terms of the underlying distribution function. The novel modeling approach is motivated by the analysis of a single gene regulatory module with three sources of stochasticity: intermittent gene activity, mRNA transcription/decay and protein translation/decay noise. Although the corresponding Chapman Kolmogorov equation cannot be solved when a large number of molecules are considered, it is used to analytically derive the first two moments of the underlying distribution function. The mRNA and protein variance is found decomposable into additive terms resulting from the respective sources of stochasticity, which allow quantifying their significance in the process. The variance decomposition is asserted by constructing two approximations that establish a novel modeling approach: First, the continuous approximation, which considers only the stochasticity due to the intermittent gene activity. Second, the mixed approximation, which in addition attributes stochasticity to the mRNA transcription/decay process. Introduced approximations yield systems of first order partial differential equations for the underlying distribution function, which can be efficiently solved using developed numerical methods. Single cell simulations and numerical two-dimensional mRNA-protein stationary distribution functions are presented to confirm accuracy of introduced models. Further simplifications in the model allow considering regulation of the two- (possibly three-) gene systems for which two-dimensional protein-protein distributions are calculated. Finally, the assumption that gene activity is due to the binding and dissociation of a single regulatory molecule is relaxed. Based on the gene expression data, the models developed are applied to hypothesize the existence of a sequential activation mechanism of NF-kappaB dependent genes important in cell survival and inflammation. Future applications include analysis of small genetic networks, which are being currently engineered based on the prokaryotic and eukaryotic components.
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    Stability of bacterial toggle switches is enhanced by cell-cycle lengthening by several orders of magnitude
    (American Physical Society, 2014) Jaruszewicz, Joanna; Kimmel, Marek; Lipniacki, Tomasz; Bioengineering; Statistics
    Bistable regulatory elements are important for nongenetic inheritance, increase of cell-to-cell heterogeneity allowing adaptation, and robust responses at the population level. Here, we study computationally the bistable genetic toggle switch-a small regulatory network consisting of a pair of mutual repressors-in growing and dividing bacteria. We show that as cells with an inhibited growth exhibit high stability of toggle states, cell growth and divisions lead to a dramatic increase of toggling rates. The toggling rates were found to increase with rate of cell growth, and can be up to six orders of magnitude larger for fast growing cells than for cells with the inhibited growth. The effect is caused mainly by the increase of protein and mRNA burst sizes associated with the faster growth. The observation that fast growth dramatically destabilizes toggle states implies that rapidly growing cells may vigorously explore the epigenetic landscape enabling nongenetic evolution, while cells with inhibited growth adhere to the local optima. This can be a clever population strategy that allows the slow growing (but stress resistant) cells to survive long periods of unfavorable conditions. Simultaneously, at favorable conditions, this stress resistant (but slowly growing?or not growing) subpopulation may be replenished due to a high switching rate from the fast growing population.