Dataset Information

Identifying parameters of host cell vulnerability during Salmonella infection by quantitative image analysis and modeling.

ABSTRACT: Salmonella targets and enters epithelial cells at permissive entry sites: some cells are more likely to be infected than others. However, the parameters that lead to host cell heterogeneity are not known. Here, we quantitatively characterized host cell "vulnerability" towards Salmonella infection based on imaged parameters. We performed successive infections of the same host cell population followed by automated high-throughput microscopy and observed that infected cells have higher probability of being re-infected. Establishing a predictive model we identified two combined origins of host cell vulnerability: the pathogen-induced cellular vulnerability emerging from Salmonella uptake and persisting at later stage of the infection, and the host cell-inherent vulnerability. We linked the host cell inherent vulnerability with its morphological attributes such as the local cell crowding, and with host cell cholesterol content. This showed that the probability of Salmonella infection success can be forecast from morphological or molecular host cell parameters.

SUBMITTER: Voznica J

PROVIDER: S-EPMC5736804 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

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Publications

Identification of Parameters of Host Cell Vulnerability during Salmonella Infection by Quantitative Image Analysis and Modeling.

Voznica Jakub J Gardella Christophe C Belotserkovsky Ilia I Dufour Alexandre A Enninga Jost J Stévenin Virginie V

Infection and immunity 20171219 1

<i>Salmonella</i> targets and enters epithelial cells at permissive entry sites: some cells are more likely to be infected than others. However, the parameters that lead to host cell heterogeneity are not known. Here, we quantitatively characterized host cell vulnerability to <i>Salmonella</i> infection based on imaged parameters. We performed successive infections of the same host cell population followed by automated high-throughput microscopy and observed that infected cells have a higher pro ...[more]

PMID: 29084895

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Project description:Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely stratification of sepsis patients by distinguishing hyper-inflammatory from paralytic phases in immune dysregulation.

Dataset Information

Identifying parameters of host cell vulnerability during Salmonella infection by quantitative image analysis and modeling.

Publications

Identification of Parameters of Host Cell Vulnerability during Salmonella Infection by Quantitative Image Analysis and Modeling.

Similar Datasets

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