@article{BencurovaGuptaSarukhanyanetal.2018,
  author    = {Bencurova, Elena and Gupta, Shishir K. and Sarukhanyan, Edita and Dandekar, Thomas},
  title     = {Identification of antifungal targets based on computer modeling},
  series = {Journal of Fungi},
  volume    = {4},
  journal   = {Journal of Fungi},
  number    = {3},
  issn      = {2309-608X},
  doi       = {10.3390/jof4030081},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197670},
  pages     = {81},
  year      = {2018},
  abstract  = {Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host-pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.},
  language  = {en}
}
@article{YuWolfThuseketal.2021,
  author    = {Yu, Yidong and Wolf, Ann-Katrin and Thusek, Sina and Heinekamp, Thorsten and Bromley, Michael and Krappmann, Sven and Terpitz, Ulrich and Voigt, Kerstin and Brakhage, Axel A. and Beilhack, Andreas},
  title     = {Direct Visualization of Fungal Burden in Filamentous Fungus-Infected Silkworms},
  series = {Journal of Fungi},
  volume    = {7},
  journal   = {Journal of Fungi},
  number    = {2},
  issn      = {2309-608X},
  doi       = {10.3390/jof7020136},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228855},
  year      = {2021},
  abstract  = {Invasive fungal infections (IFIs) are difficult to diagnose and to treat and, despite several available antifungal drugs, cause high mortality rates. In the past decades, the incidence of IFIs has continuously increased. More recently, SARS-CoV-2-associated lethal IFIs have been reported worldwide in critically ill patients. Combating IFIs requires a more profound understanding of fungal pathogenicity to facilitate the development of novel antifungal strategies. Animal models are indispensable for studying fungal infections and to develop new antifungals. However, using mammalian animal models faces various hurdles including ethical issues and high costs, which makes large-scale infection experiments extremely challenging. To overcome these limitations, we optimized an invertebrate model and introduced a simple calcofluor white (CW) staining protocol to macroscopically and microscopically monitor disease progression in silkworms (Bombyx mori) infected with the human pathogenic filamentous fungi Aspergillus fumigatus and Lichtheimia corymbifera. This advanced silkworm A. fumigatus infection model could validate knockout mutants with either attenuated, strongly attenuated or unchanged virulence. Finally, CW staining allowed us to efficiently visualize antifungal treatment outcomes in infected silkworms. Conclusively, we here present a powerful animal model combined with a straightforward staining protocol to expedite large-scale in vivo research of fungal pathogenicity and to investigate novel antifungal candidates.},
  language  = {en}
}