Project B2

Signaling molecules as entry tickets for infection networks in Aspergillus fumigatus and Candida albicans human infection

Project Description

Reconstruction and logical connectivity (Dandekar, left) is combined with analysis of control points K (Kaleta; Wesseley et al. 2011) and integration of new components from interactome databanks (Dittrich, white circles; Boyanova et al. 2012). Semi-quantitative modelling (Naseem et al., 2012) includes pathogen signals (middle) and host response (peaked curves, right; Dandekar), dynamical validation of regulation (Kaleta) as well as system state analysis of the interaction network (Dittrich).

The human-pathogenic fungi Aspergillus fumigatus and Candida albicans use a variety of different signalling molecules and pathways for the interaction with their host. The resulting immune response employs typical mediators and signalling pathways. In this project, by exploiting previous experience, methods and results, we will describe and model in an advanced way signalling interactions between pathogenic fungi and the human host. Signalling molecules and pathways will be collected from databases, bioinformatical analysis, public data and current experiments in the CRC/Transregio. Data will be assembled and compared by sequence and domain analysis in terms of evolution, functional domains and signalling properties. Furthermore, interconnections will be systematically predicted (structure analysis, domain analysis, known and predicted modifications, interactions known from other organisms or tissues, cluster and network predictions etc.). Resulting networks will be analysed (typical host- and pathogen-specific modules). Component assembly leads first to Boolean networks, next to prediction of network effects, analysis of their connectivity, switching behaviour and system states. This analysis will be followed by semi-quantitative dynamical modelling by applying simulation software. Based on these results and combined with an approach to identify nodes in the interaction network that are central for signal processing and which will be also iteratively refined in the light of available experimental data, the impact of the different components and signalling pathways on the host-pathogen interaction will be better understood and described. Hypotheses will be verified experimentally in the CRC/Transregio, generating knowledge-based signalling networks of the host-pathogen interaction for (i) Aspergillus fumigatus and (ii) Candida albicans with human cells. Predictions validated by the experimental collaboration partners will be evaluated for their medical implications (e.g. fungal pathogen-host signalling in evasion, adherence, barrier breakage, sepsis) in collaboration with the clinical partners of the CRC/Transregio.

Principal Investigators

Prof. Dr. Thomas Dandekar
Prof. Dr. Thomas Dandekar

Biocenter, Department of Bioinformatics and Research Center for Infectious Diseases (ZINF)  

Julius Maximilians University Würzburg

Dr. Dr. Marcus Dittrich
Dr. Dr. Marcus Dittrich

Biocenter, Department of Bioinformatics and Research Center for Infectious Diseases (ZINF)  

Julius Maximilians University Würzburg


Author Year Title Journal Links
Remmele WC, Luther C, Balkenhol J, Dandekar T, Mueller T, Dittrich MT 2015

Integrated inference and evaluation of host-fungi interaction networks.

Front Micobiol 6: 764

Ewald J, Kötzing M, Bartl M, Kaleta C 2015

Footprints of optimal protein assembly strategies in the operonic structure of prokaryotes.

Metabolites 5: 252-69 PubMed

de Hijas-Liste GM, Balsa-Canto E, Ewald J, Bartl M, Li P, Banga JR, Kaleta C


Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation.

BMC Bioinformatics 16: 163 BMC Bioinformatics
Morton CO, Fliesser M, Dittrich M, Mueller T, Bauer R, Kneitz S, Hope W, Rogers TR, Einsele H, Loeffler J 2014 Gene expression profiles of human dendritic cells interacting with Aspergillus fumigatus in a bilayer model of the alveolar epithelium/endothelium interface. PLoS One 9: e98279 PubMed
Boyanova D, Nilla S, Klau GW, Dandekar T, Muller T, Dittrich M 2014 Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data. Mol Cell Proteomics 13: 1877-89 PubMed
Gupta SK, Gross R, Dandekar T 2016 An antibiotic target ranking and prioritization pipeline ombining sequence, structure and network-based approaches exemplified for Serratia marcescens.

Gene 591: 268-78

Czakai K, Dittrich M, Kaltdorf M, Müller T, Krappmann S, Schedler A, Bonin M, Dühring S, Schuster S, Speth C, Rambach G, Einsele H, Dandekar T, Löffler J 2016 Influence of platelet-rich plasma on the immune response of human monocyte-derived dendritic cells and macrophages stimulated with Aspergillus fumigatus.

Int J Med Microbiol pii: S1438-4221(16)30199-0


Duell J, Dittrich M, Bedke T, Mueller T, Eisele F, Rosenwald A, Rasche L, Hartmann E, Dandekar T, Einsele H, Topp M


Frequency of regulatory T cells determines the outcome of the T cell engaging antibody blinatumomab in patients with B precursor ALL.

Leukemia doi: 10.1038/leu.2017.41 [Epub ahead of print] PubMed
Ewald J, Bartl M, Dandekar T, Kaleta C 2017 Optimality principles reveal a complex interplay of intermediate toxicity and kinetic efficiency in the regulation of prokaryotic metabolism. PLoS Comput Biol 13: e1005371 PubMed
Dühring S, Ewald J, Germerodt S, Kaleta C, Dandekar T, Schuster S 2017 Modelling the host-pathogen interactions of macrophages and Candida albicans using game theory and dynamic optimisation. J R Soc Interface 14: pii: 20170095
Ewald J, Bartl M, Kaleta C 2017 Deciphering the regulation of metabolism with dynamic optimization: an overview of recent advances.

Biochem Soc Trans pii: BST20170137