Nonmammalian model systems to investigate fungal biofilms

Marios Arvanitis1, Beth Burgwyn Fuchs, Eleftherios Mylonakis

  • 1Infectious Diseases Division, Department of Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, Suite 301, Providence, RI, 02903, USA.

Insights

Investigating fungal filamentation in invertebrate models like Galleria mellonella is crucial for understanding biofilm development and combating difficult-to-treat fungal infections in immunocompromised patients.

Area of Science:

  • Medical Mycology
  • Infectious Diseases
  • Host-Pathogen Interactions

Background:

  • Increased prevalence of immunocompromised patients and medical devices leads to higher risk of fungal infections.
  • Fungal biofilms are key to pathogenesis, enabling tissue invasion and antimicrobial resistance.
  • Effective treatment of these infections remains a significant challenge.

Purpose of the Study:

  • To review methods for investigating fungal filamentation, a critical step in biofilm formation.
  • To highlight the utility of invertebrate hosts as models for studying fungal infections in vivo.
  • To identify potential targets for antifungal therapies.

Main Methods:

  • Utilizing invertebrate models: Galleria mellonella, Caenorhabditis elegans, and Drosophila melanogaster.
  • Investigating genetic requirements for fungal filamentation within these hosts.
  • Screening for compounds that inhibit fungal filamentation.

Main Results:

  • Invertebrate models provide valuable insights into fungal pathogenesis and biofilm development.
  • Filamentation is a critical virulence factor in fungal infections.
  • Methods for studying filamentation in vivo are essential for drug discovery.

Conclusions:

  • Invertebrate models are powerful tools for studying fungal virulence and biofilm formation.
  • Understanding fungal filamentation is key to developing novel antifungal strategies.
  • This review consolidates methods for advancing antifungal research using invertebrate models.