@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}
}
@article{KaltdorfSrivastavaGuptaetal.2016,
  author    = {Kaltdorf, Martin and Srivastava, Mugdha and Gupta, Shishir K. and Liang, Chunguang and Binder, Jasmin and Dietl, Anna-Maria and Meir, Zohar and Haas, Hubertus and Osherov, Nir and Krappmann, Sven and Dandekar, Thomas},
  title     = {Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach},
  series = {Frontiers in Molecular Bioscience},
  volume    = {3},
  journal   = {Frontiers in Molecular Bioscience},
  doi       = {10.3389/fmolb.2016.00022},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147396},
  pages     = {22},
  year      = {2016},
  abstract  = {New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.},
  language  = {en}
}