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Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

ABSTRACT: 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.

SUBMITTER: Lehnert T

PROVIDER: S-EPMC4473060 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

Lehnert Teresa T Timme Sandra S Pollmächer Johannes J Hünniger Kerstin K Kurzai Oliver O Figge Marc Thilo MT

Frontiers in microbiology 20150619

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 experime ...[more]

PMID: 26150807

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Project description:The relative importance of bottom-up versus top-down control of population dynamics has been the focus of much debate. In infectious disease biology, research is typically focused on the bottom-up process of host resistance, wherein the direction of control flows from the lower to the higher trophic level to impact on pathogen population size and epidemiology. However, the importance of top-down control by a pathogen's natural enemies has been mostly overlooked.Here, we explore the effects of, and interaction between, host genotype (i.e., genetic susceptibility to pathogen infection) and infection by a hyperparasitic fungus, Ampelomyces spp., on the establishment and early epidemic growth and transmission of a powdery mildew plant pathogen (Podosphaera plantaginis). We used a semi-natural field experiment to contrast the impacts of hyperparasite infection, host-plant resistance and spatial structure to reveal the key factors that determine pathogen spread. We then used a laboratory-based inoculation approach to test whether the field experiment results hold across multiple pathogen-host genetic combinations and to explore hyperparasite effects on the pathogen's later life-history stages.We found that hyperparasite infection had a negligible effect on within-host infection development and between-host spread of the pathogen during the onset of epidemics. In contrast, host-plant resistance was the major determinant of whether plants became infected, and host genotype and proximity to an infection source determined infection severity.Our laboratory study showed that, while the interaction between host and pathogen genotypes was the key determinant of infection outcome, hyperparasitism did, on average, reduce the severity of infection. Moreover, hyperparasite infection negatively influenced the production of the pathogen's overwintering structures. Synthesis and applications. Our results suggest that bottom-up host resistance affects pathogen spread, but top-down control of powdery mildew pathogens is likely more effective against later life-history stages. Further, while hyperparasitism in this system can reduce early pathogen growth under stable laboratory conditions, this effect is not detectable in a semi-natural environment. Considering the effects of hyperparasites at multiple points in pathogen's life history will be important when considering hyperparasite-derived biocontrol measures in other natural and agricultural systems.

Project description:BackgroundAcinetobacter baumannii is a major health problem. The most common infection caused by A. baumannii is hospital acquired pneumonia, and the associated mortality rate is approximately 50%. Neither in vivo nor ex vivo expression profiling has been performed at the proteomic or transcriptomic level for pneumonia caused by A. baumannii. In this study, we characterized the proteome of A. baumannii under conditions that simulate those found in the airways, to gain some insight into how A. baumannii adapts to the host and to improve knowledge about the pathogenesis and virulence of this bacterium. A clinical strain of A. baumannii was grown under different conditions: in the presence of bronchoalveolar lavage fluid from infected rats, of RAW 264.7 cells to simulate conditions in the respiratory tract and in control conditions. We used iTRAQ labelling and LC-MALDI-TOF/TOF to investigate how A. baumannii responds on exposure to macrophages/BALF.Results179 proteins showed differential expression. In both models, proteins involved in the following processes were over-expressed: (i) pathogenesis and virulence (OmpA, YjjK); (ii) cell wall/membrane/envelope biogenesis (MurC); (iii) energy production and conversion (acetyl-CoA hydrolase); and (iv) translation (50S ribosomal protein L9). Proteins involved in the following were under-expressed: (i) lipid metabolism (short-chain dehydrogenase); (ii) amino acid metabolism and transport (aspartate aminotransferase); (iii) unknown function (DNA-binding protein); and (iv) inorganic ion transport and metabolism (hydroperoxidase).ConclusionsWe observed alterations in cell wall synthesis and identified 2 upregulated virulence-associated proteins with >15 peptides/protein in both ex vivo models (OmpA and YjjK), suggesting that these proteins are fundamental for pathogenesis and virulence in the airways. This study is the first comprehensive overview of the ex vivo proteome of A. baumannii and is an important step towards identification of diagnostic biomarkers, novel drug targets and potential vaccine candidates in the fight against pneumonia caused by A. baumannii.

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Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

Publications

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

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