B4

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Image data analysis and agent-based modelling of the spatio-temporal interaction between immune cells and human-pathogenic fungi

Programme: Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

SEEK ID: https://funginet.hki-jena.de/projects/27

Public web page: http://www.funginet.de/project-b4.html

Organisms: Aspergillus fumigatus, Candida albicans

INF PALs: No PALs for this Project

Project created: 21st Jul 2015

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Showing 5 out of a possible 11 People Advanced People list for this Project with search and filtering

Stefanie Dietrich : inactive since 2020-06-09

Projects: B4, FungiNet total, FungiNet B - Bioinformatics projects

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

Expertise: Image analysis

Tools: Python, openCV

Marc Thilo Figge

Projects: FungiNet total, FungiNet B - Bioinformatics projects, B4

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI), Friedrich-Schiller-University

https://orcid.org/0000-0002-4044-9166

Expertise: Image-based Systems Biology, Candida albicans, Aspergillus fumigatus, Lichtheimia corymbifera

Tools: Image analysis, Mathematical Modelling, Computational and theoretical biology

Peter Großmann

: inactive since 2020-06-01

Projects: FungiNet total, INF, A1, A2, A3, A4 (E), A5, A6, B1, B3 (E), B4, B5, C1, C2, C3, C4 (E), C5, C6 (E), Z1, Z2, FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, B2

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

https://orcid.org/0000-0001-6106-724X

Expertise: Metabolic networks, Transcriptional Regulation, Bioinformatics, Integrative Data Analysis, Machine Learning, DNA supercoiling

Tools: LaTeX, Matlab, R, Systems biological modelling

Kaswara Kraibooj : inactive since 2018-06-07

Projects: B4

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

Teresa Lehnert

Projects: B4

Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

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Collaborative Research Center / Transregio 124 "Pathogenic fungi and their human host: Networks of Interaction - FungiNet"

The yeast Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of life-threatening invasive mycoses in Europe. Despite the increasing incidence of these infections, the current diagnosis is still difficult and often too late, and options for therapies are limited. Moreover, A. fumigatus and C. albicans have developed multiple sophisticated, specific and unique pathogenicity mechanisms. Many of these mechanisms are not well understood. Research on ...

Projects: FungiNet total, FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF, A1, A2, A3, A4 (E), A5, B1, B2, B3 (E), B4, C1, C2, C3, C4 (E), C5, C6 (E), Z2, Z1, B5, A6, A7, A8, C7

Web page: http://www.funginet.de/home.html

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Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

Country: Germany

City: 07745 Jena

Web page: http://www.hki-jena.de

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Showing 5 out of a possible 9 Models Advanced Models list for this Project with search and filtering

Cell track generator

README

This package contains code that generates and analyses tracks in 2D given three distinct condition; non-persistent, persistent and directed by the Ornstein-Uhlenbeck process. Full details on the analysis can be found in the article "Svensson et al., Untangling cell tracks: quantifying cell migration by time lapse image data analysis, Cytometry Pt A, 2017". The paper can be found on ...

Creator: Carl-Magnus Svensson

Submitter: Carl-Magnus Svensson

Model type: Ordinary differential equations (ODE)

Model format: Not specified

Environment: Not specified

Organism: Not specified

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

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Created: 19th Feb 2021 at 09:06

State-based model of whole-blood infection assay with PMN-mediated immune evasion mechanism

This model is a variant of the previously developed SBM of whole-blood infection assay. While in the previous model a spontaneous immune evasion mechanism (A) was implemented in this model immune evasion was caused by PMN molecule secretion (B).

Creators: Teresa Lehnert, Maria T. E. Prauße

Submitter: Sandra Timme

Model type: Not specified

Model format: Not specified

Environment: Not specified

Organism: Candida albicans

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 18th Feb 2021 at 09:28

State-based model of whole-blood infection assays with Candida albicans

The state-based model (SBM) allow to simulate the interplay between innate immune cells, such as monocytes and PMN, and Candida albicans and to quantify immune reaction rates like phagocytosis and killing rates. It is implemented in C++.

Creator: Teresa Lehnert

Submitter: Sandra Timme

Model type: Not specified

Model format: Not specified

Environment: Not specified

Organism: Candida albicans

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 18th Feb 2021 at 09:23, Last updated: 18th Feb 2021 at 09:23

DynaCoSys

DynaCoSys models the complement system by using a combination of Ordinary and Partial Differential Equations.

Creators: Teresa Lehnert, Alexander Tille

Submitter: Sandra Timme

Model type: Partial differential equations (PDE)

Model format: Not specified

Environment: Not specified

Organism: Candida albicans

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 16th Feb 2021 at 16:25, Last updated: 18th Feb 2021 at 09:00

Virtual infection model of Aspergillus fumigatus in a murine alveolus

Adaptation of the previously developed virtual infection model of Aspergillus fumigatus in a human alveolus. This model comprises a hybrid agent-based model of a single murine alveolus. The alveolus is represented in a realistic to-scale representation and contains the cell types of alveolar epithelial cells (AEC) of type 1 and 2 as well as the pores of Kohn (PoK). Furthermore, in this model, depending on the infection dose multiple A. fumigatus conidium are inserted into the alveolus and the ...

Creators: Sandra Timme, Marco Blickensdorf

Submitter: Sandra Timme

Model type: Agent based modelling

Model format: Not specified

Environment: Not specified

Organism: Aspergillus fumigatus

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 18th Feb 2021 at 08:57, Last updated: 18th Feb 2021 at 08:57

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Showing 5 out of a possible 26 Publications Advanced Publications list for this Project with search and filtering

Quantification of Factor H Mediated Self vs. Non-self Discrimination by Mathematical Modeling.

FungiNet C - Candida projects, B4

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Abstract (Expand)

The complement system is part of the innate immune system and plays an important role in the host defense against infectious pathogens. One of the main effects is the opsonization of foreign invaders … and subsequent uptake by phagocytosis. Due to the continuous default basal level of active complement molecules, a tight regulation is required to protect the body's own cells (self cells) from opsonization and from complement damage. A major complement regulator is Factor H, which is recruited from the fluid phase and attaches to cell surfaces where it effectively controls complement activation. Besides self cells, pathogens also have the ability to bind Factor H; they can thus escape opsonization and phagocytosis causing severe infections. In order to advance our understanding of the opsonization process at a quantitative level, we developed a mathematical model for the dynamics of the complement system-termed DynaCoSys model-that is based on ordinary differential equations for cell surface-bound molecules and on partial differential equations for concentration profiles of the fluid phase molecules in the environment of cells. This hybrid differential equation approach allows to model the complement cascade focusing on the role of active C3b in the fluid phase and on the cell surface as well as on its inactivation on the cell surface. The DynaCoSys model enables us to quantitatively predict the conditions under which Factor H mediated complement evasion occurs. Furthermore, investigating the quantitative impact of model parameters by a sensitivity analysis, we identify the driving processes of complement activation and regulation in both the self and non-self regime. The two regimes are defined by a critical Factor H concentration on the cell surface and we use the model to investigate the differential impact of complement model parameters on this threshold value. The dynamic modeling on the surface of pathogens are further relevant to understand pathophysiological situations where Factor H mutants and defective Factor H binding to target surfaces results in pathophysiology such as renal and retinal disease. In the future, this DynaCoSys model will be extended to also enable evaluating treatment strategies of complement-related diseases.

Authors: A. Tille, T. Lehnert, P. F. Zipfel, M. T. Figge

Date Published: 5th Oct 2020

Publication Type: Not specified

PubMed ID: 33013842

Citation: Front Immunol. 2020 Sep 2;11:1911. doi: 10.3389/fimmu.2020.01911. eCollection 2020.

Created: 16th Feb 2021 at 16:08, Last updated: 17th Jan 2024 at 10:24

Hybrid Agent-Based Modeling of Aspergillus fumigatus Infection to Quantitatively Investigate the Role of Pores of Kohn in Human Alveoli.

Abstract (Expand)

The healthy state of an organism is constantly threatened by external cues. Due to the daily inhalation of hundreds of particles and pathogens, the immune system needs to constantly accomplish the task …

Authors: M. Blickensdorf, S. Timme, M. T. Figge

Date Published: 9th Sep 2020

Publication Type: Not specified

PubMed ID: 32903715

Citation: Front Microbiol. 2020 Aug 12;11:1951. doi: 10.3389/fmicb.2020.01951. eCollection 2020.

Created: 11th Feb 2021 at 16:07, Last updated: 17th Jan 2024 at 10:24

Flotillin-Dependent Membrane Microdomains Are Required for Functional Phagolysosomes against Fungal Infections.

FungiNet A - Aspergillus projects, B4

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Abstract (Expand)

Lipid rafts form signaling platforms on biological membranes with incompletely characterized role in immune response to infection. Here we report that lipid-raft microdomains are essential components …

Authors: F. Schmidt, A. Thywissen, M. Goldmann, C. Cunha, Z. Cseresnyes, H. Schmidt, M. Rafiq, S. Galiani, M. H. Graler, G. Chamilos, J. F. Lacerda, A. Jr Campos, C. Eggeling, M. T. Figge, T. Heinekamp, S. G. Filler, A. Carvalho, A. A. Brakhage

Date Published: 18th Aug 2020

Publication Type: Not specified

PubMed ID: 32814035

Citation: Cell Rep. 2020 Aug 18;32(7):108017. doi: 10.1016/j.celrep.2020.108017.

Created: 16th Feb 2021 at 16:12, Last updated: 17th Jan 2024 at 10:24

Functional surface proteomic profiling reveals the host heat-shock protein A8 as a mediator of Lichtheimia corymbifera recognition by murine alveolar macrophages.

A1, A5, B4, Z2, A6

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Abstract (Expand)

Mucormycosis is an emergent, fatal fungal infection of humans and warm-blooded animals caused by species of the order Mucorales. Immune cells of the innate immune system serve as the first line of …

Authors: M. I. A. Hassan, J. M. Kruse, T. Kruger, H. M. Dahse, Z. Cseresnyes, M. G. Blango, H. Slevogt, F. Horhold, V. Ast, R. Konig, M. T. Figge, O. Kniemeyer, A. A. Brakhage, K. Voigt

Date Published: 26th Jun 2020

Publication Type: Not specified

PubMed ID: 32583550

Citation: Environ Microbiol. 2020 Sep;22(9):3722-3740. doi: 10.1111/1462-2920.15140. Epub 2020 Jul 21.

Created: 15th Feb 2021 at 10:04, Last updated: 17th Jan 2024 at 10:24

Quantitative Impact of Cell Membrane Fluorescence Labeling on Phagocytosis Measurements in Confrontation Assays.

FungiNet A - Aspergillus projects, B4

Abstract (Expand)

Phagocytosis is series of steps where the pathogens and the immune cells interact during an invasion. This starts with the adhesion process between the host and pathogen cells, and is followed by the … engulfment of the pathogens. Many analytical methods that are applied to characterize phagocytosis based on imaging the host-pathogen confrontation assays rely on the fluorescence labeling of cells. However, the potential effect of the membrane labeling on the quantitative results of the confrontation assays has not been studied in detail. In this study, we determine whether the fluorescence labeling processes themselves influence the results of the phagocytosis measurements. Here, alveolar macrophages, which form one of the most important compartments of the innate immune system, were used as an example of host cells, whereas Aspergillus fumigatus and Lichtheimia corymbifera that cause aspergillosis and mucormycosis, respectively, were studied as examples for pathogens. At first, our study investigated the importance of the sequence of steps of the fixation process when preparing the confrontation assay sample for microscopy studies. Here we showed that applying the fixation agent before the counter-staining causes miscalculations during the determination of the phagocytic measures. Furthermore, we also found that staining the macrophages with various concentrations of DID, as a typical membrane label, in most cases altered the capability of macrophages to phagocytose FITC-stained A. fumigatus and L. corymbifera spores in comparison with unlabeled macrophages. This effect of the DID staining showed a differential character dependent upon the labeling status and the specific type of pathogen. Moreover, labeling the spores of A. fumigatus and L. corymbifera with FITC increased the phagocytic measures during confrontation with unlabeled macrophages when compared to label-free spores. Overall, our study confirms that the staining process itself may significantly manipulate the quantitative outcome of the confrontation assay. As a result of our study, we also developed a user-friendly image analysis tool that analyses confrontation assays both with and without fluorescence labeling of the host cells and of the pathogens. Our image analysis algorithm saves experimental work effort and time, provides more precise results when calculating the phagocytic measures, and delivers a convenient analysis tool for the biologists to monitor host-pathogen interactions as they happen without the artifacts that fluorescence labeling imposes on biological interactions.

Authors: Z. Cseresnyes, M. I. A. Hassan, H. M. Dahse, K. Voigt, M. T. Figge

Date Published: 26th Jun 2020

Publication Type: Not specified

PubMed ID: 32582113

Citation: Front Microbiol. 2020 Jun 5;11:1193. doi: 10.3389/fmicb.2020.01193. eCollection 2020.

Created: 16th Feb 2021 at 15:46, Last updated: 17th Jan 2024 at 10:24

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