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Schuster, S.

Publications and source records attributed to Schuster, S..

2 recordsLinked to original sources

Molecular crypsis by pathogenic fungi using human factor H. A numerical model

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made, which can lead to autoreactivity. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans. We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement associated autoimmune diseases.

systems biology

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.

The identification of disease associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. A major problem hampering their identification is the detection of protein communities within large-scale, whole-genome PPINs. Current strategies solve the maximal clique enumeration (MCE) problem, i.e., the enumeration of all non-extendable groups of proteins, where each pair of proteins is connected by an edge. The MCE problem however is non-deterministic polynomial time hard and can thus be computationally overwhelming for large-scale, whole-genome PPINs.\n\nWe present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs in conjunction with gene-expression data. ModuleDiscoverer is a heuristic that approximates the community structure underlying PPINs. Based on a high-confidence PPIN of Rattus norvegicus and publicly available gene expression data we apply our algorithm to identify the regulatory module of a rat-model of diet induced non-alcoholic steatohepatitis (NASH). We validate the module using single-nucleotide polymorphism data from independent genome-wide association studies. Structural analysis of the module reveals 10 sub-modules. These sub-modules are associated with distinct biological functions and pathways that are relevant to the pathological and clinical situation in NASH.\n\nModuleDiscoverer is freely available upon request from the corresponding author.

systems biology