@article{RemmeleLutherBalkenholetal.2015,
  author    = {Remmele, Christian W. and Luther, Christian H. and Balkenhol, Johannes and Dandekar, Thomas and M{\"u}ller, Tobias and Dittrich, Marcus T.},
  title     = {Integrated inference and evaluation of host-fungi interaction networks},
  series = {Frontiers in Microbiology},
  volume    = {6},
  journal   = {Frontiers in Microbiology},
  number    = {764},
  doi       = {10.3389/fmicb.2015.00764},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148278},
  year      = {2015},
  abstract  = {Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi human and fungi mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host fungi transcriptome and proteome data.},
  language  = {en}
}
@article{DuehringGermerodtSkerkaetal.2015,
  author    = {D{\"u}hring, Sybille and Germerodt, Sebastian and Skerka, Christine and Zipfel, Peter F. and Dandekar, Thomas and Schuster, Stefan},
  title     = {Host-pathogen interactions between the human innate immune system and Candida albicans - understanding and modeling defense and evasion strategies},
  series = {Frontiers in Microbiology},
  volume    = {6},
  journal   = {Frontiers in Microbiology},
  number    = {625},
  doi       = {10.3389/fmicb.2015.00625},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151621},
  year      = {2015},
  abstract  = {The diploid, polymorphic yeast Candida albicans is one of the most important human pathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within the human host for a long time. However, alterations in the host environment can render C. albicans virulent. In this review, we describe the immunological cross-talk between C. albicans and the human innate immune system. We give an overview in form of pairs of human defense strategies including immunological mechanisms as well as general stressors such as nutrient limitation, pH, fever etc. and the corresponding fungal response and evasion mechanisms. Furthermore, Computational Systems Biology approaches to model and investigate these complex interactions are highlighted with a special focus on game-theoretical methods and agent-based models. An outlook on interesting questions to be tackled by Systems Biology regarding entangled defense and evasion mechanisms is given.},
  language  = {en}
}