Volatile Fingerprint Mediates Yeast-to-Mycelial Conversion in Two Strains of Beauveria bassiana Exhibiting Varied Virulence.
Arturo Ramírez-OrdoricaJosé Alberto Patiño-MedinaVíctor Meza-CarmenLourdes Macías-RodríguezPublished in: Journal of fungi (Basel, Switzerland) (2023)
Beauveria bassiana is a dimorphic and entomopathogenic fungus with different ecological roles in nature. In pathogenic fungi, yeast-to-mycelial conversion, which is controlled by environmental factors, is required for virulence. Here, we studied the effects of different stimuli on the morphology of two B. bassiana strains and compared the toxicities of culture filtrates. In addition, we explored the role of volatiles as quorum sensing-like signals during dimorphic transition. The killing assays in Caenorhabditis elegans (Nematoda: Rhabditidae) showed that strain AI2 isolated from a mycosed insect cadaver had higher toxicity than strain AS5 isolated from soil. Furthermore, AI2 showed earlier yeast-to-mycelial switching than AS5. However, an increase in inoculum size induced faster yeast-to-mycelium conversion in AS5 cells, suggesting a cell-density-dependent phenomenon. Gas chromatography-mass spectrometry (GC-MS) analyses showed that the fingerprint of the volatiles was strain-specific; however, during the morphological switching, an inverse relationship between the abundance of total terpenes and 3-methylbutanol was observed in both strains. Fungal exposure to 3-methylbutanol retarded the yeast-to-mycelium transition. Hence, this study provides evidence that volatile compounds are associated with critical events in the life cycle of B. bassiana .
Keyphrases
- gas chromatography mass spectrometry
- escherichia coli
- saccharomyces cerevisiae
- cell wall
- gas chromatography
- pseudomonas aeruginosa
- life cycle
- staphylococcus aureus
- artificial intelligence
- induced apoptosis
- biofilm formation
- oxidative stress
- mass spectrometry
- cystic fibrosis
- solid phase extraction
- high throughput
- deep learning
- cell therapy
- signaling pathway
- diabetic rats
- high glucose
- machine learning
- cell death
- mesenchymal stem cells
- endoplasmic reticulum stress
- bone marrow
- risk assessment
- quality control
- human health