TY  - JOUR
A1  - Yu, Yidong
A1  - Wolf, Ann-Katrin
A1  - Thusek, Sina
A1  - Heinekamp, Thorsten
A1  - Bromley, Michael
A1  - Krappmann, Sven
A1  - Terpitz, Ulrich
A1  - Voigt, Kerstin
A1  - Brakhage, Axel A.
A1  - Beilhack, Andreas
T1  - Direct Visualization of Fungal Burden in Filamentous Fungus-Infected Silkworms
JF  - Journal of Fungi
N2  - 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.
KW  - fungal infection model
KW  - calcofluor white staining
KW  - Aspergillus
KW  - Lichtheimia
KW  - silkworm
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-228855
SN  - 2309-608X
VL  - 7
IS  - 2
ER  - 
TY  - JOUR
A1  - Kaltdorf, Martin
A1  - Srivastava, Mugdha
A1  - Gupta, Shishir K.
A1  - Liang, Chunguang
A1  - Binder, Jasmin
A1  - Dietl, Anna-Maria
A1  - Meir, Zohar
A1  - Haas, Hubertus
A1  - Osherov, Nir
A1  - Krappmann, Sven
A1  - Dandekar, Thomas
T1  - Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach
JF  - Frontiers in Molecular Bioscience
N2  - 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.
KW  - metabolism
KW  - targets
KW  - antimycotics
KW  - modeling
KW  - structure
KW  - interaction
KW  - fungicide
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147396
VL  - 3
ER  -