The dynamics of Cryptococcus neoformans infection in Galleria mellonella - Daniel F. Q. Smith; Aviv Bergman; Arturo Casadevall (mSphere)
ABSTRACT Galleria mellonella has emerged as an important host for the study of fungal virulence, insect immune responses, and the evaluation of antifungal agents. In this study, we investigated the dynamics of fungal infections in G. mellonella using Cryptococcus neoformans, a human pathogenic fungus. Since the analysis of infection dynamics requires a fine temporal resolution of larval death, we employed a photographic time-lapse technique that allowed us to simultaneously measure death by proxy of larval melanization and absence of movement. Larval mortality occurred in two phases, early and late, which differed in their timing of melanization. Early phase deaths occurred with rapid whole-body onset of melanization, followed by sudden cessation of movement several hours later. Contrastingly, late phase deaths occurred with a gradual cessation of movement, followed by melanization, typically radiating from one location on the larva. The differences in mortality kinetics suggest differences in fungal pathogenesis, with one population succumbing early while the rest linger for later death. Subsequent analysis of mortality data using the inversion method revealed predictable deterministic dynamics but did not observe evidence of chaotic signatures. While this does not preclude the existence of chaos, it indicates that this C. neoformans-G. mellonella infection model may behave differently than bacterial-insect models.IMPORTANCEThe ability to predict the course of an infection is critical in anticipating disease progression and effectively treating patients. Similarly, the ability to make predictions about pathogenesis in laboratory infection models could further our understanding of pathogenesis and lead to new treatments. As fungal diseases are expected to rise, understanding the dynamics of fungal infections will be important to anticipate and mitigate future threats. Here, we developed a time-lapse method to visualize infections of Galleria mellonella larvae with the fungal pathogen Cryptococcus neoformans. This method provided insight into infection progression that is not apparent from standard survival measurement protocols, including the relationship between melanization and death. Further, it enabled us to explore the dynamics of disease progression in this system, which revealed deterministic dynamics without evidence of chaos, implying predictability in the outcome of cryptococcal infection in this moth.