……中的感染动态
The dynamics of infection in .
作者信息
Smith Daniel F Q, Bergman Aviv, Casadevall Arturo
机构信息
Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland, USA.
Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, New York, USA.
出版信息
mSphere. 2025 Jun 25;10(6):e0019025. doi: 10.1128/msphere.00190-25. Epub 2025 May 16.
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 using , 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 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 larvae with the fungal pathogen . 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.
已成为研究真菌毒力、昆虫免疫反应和抗真菌剂评估的重要宿主。在本研究中,我们使用一种人类致病真菌,研究了其在 中的真菌感染动态。由于感染动态分析需要对幼虫死亡有精细的时间分辨率,我们采用了延时摄影技术,使我们能够通过幼虫黑化和无运动情况来同时测量死亡情况。幼虫死亡率分为早期和晚期两个阶段,黑化时间不同。早期死亡伴随着全身迅速黑化,数小时后突然停止运动。相比之下,晚期死亡则是运动逐渐停止,随后黑化,通常从幼虫的一个部位开始扩散。死亡率动力学的差异表明真菌致病机制存在差异,一部分群体早期死亡,而其余群体则延迟到后期死亡。随后使用反演方法对死亡率数据进行分析,揭示了可预测的确定性动态,但未观察到混沌特征的证据。虽然这并不排除混沌的存在,但表明这种感染模型的行为可能与细菌-昆虫模型不同。
重要性
预测感染进程的能力对于预测疾病进展和有效治疗患者至关重要。同样,在实验室感染模型中预测致病机制的能力可以加深我们对致病机制的理解,并带来新的治疗方法。随着真菌疾病预计会增加,了解真菌感染的动态对于预测和减轻未来威胁将非常重要。在这里,我们开发了一种延时方法来可视化真菌病原体对 幼虫的感染。这种方法提供了从标准存活测量方案中不明显的感染进展见解,包括黑化与死亡之间的关系。此外,它使我们能够探索该系统中疾病进展的动态,揭示了确定性动态且无混沌证据,这意味着这种蛾类隐球菌感染的结果具有可预测性。