Targeting Sterylglucosidase A to Treat Aspergillus fumigatus Infections.

Invasive fungal infections are a leading cause of death in immunocompromised patients. Current therapies have several limitations, and innovative antifungal agents are critically needed. Previously, we identified the fungus-specific enzyme sterylglucosidase as essential for pathogenesis and virulence of Cryptococcus neoformans and Aspergillus fumigatus (Af) in murine models of mycoses. Here, we developed Af sterylglucosidase A (SglA) as a therapeutic target. We identified two selective inhibitors of SglA with distinct chemical scaffolds that bind in the active site of SglA. Both inhibitors induce sterylglucoside accumulation and delay filamentation in Af and increase survival in a murine model of pulmonary aspergillosis. Structure-activity relationship (SAR) studies identified a more potent derivative that enhances both in vitro phenotypes and in vivo survival. These findings support sterylglucosidase inhibition as a promising antifungal approach with broad-spectrum potential. IMPORTANCE Invasive fungal infections are a leading cause of death in immunocompromised patients. Aspergillus fumigatus is a fungus ubiquitously found in the environment that, upon inhalation, causes both acute and chronic illnesses in at-risk individuals. A. fumigatus is recognized as one of the critical fungal pathogens for which a substantive treatment breakthrough is urgently needed. Here, we studied a fungus-specific enzyme, sterylglucosidase A (SglA), as a therapeutic target. We identified selective inhibitors of SglA that induce accumulation of sterylglucosides and delay filamentation in A. fumigatus and increase survival in a murine model of pulmonary aspergillosis. We determined the structure of SglA, predicted the binding poses of these inhibitors through docking analysis, and identified a more efficacious derivative with a limited SAR study. These results open several exciting avenues for the research and development of a new class of antifungal agents targeting sterylglucosidases.

Host populations, challenges, and commercialization of cryptococcal vaccines.

Vaccines are one of the most effective public health tools to prevent and manage infectious diseases. Since the first clinical use of vaccines in the late 18th century, many vaccines have been successfully developed to combat bacterial and viral infections, including the most recent Coronavirus Disease 2019 (COVID-19) pandemic. However, there remains no vaccine that is clinically available to treat or prevent invasive fungal diseases, including cryptococcal meningoencephalitis. This fungal disease is uniformly fatal without treatment and has a global mortality rate of over 70%. Despite a dire need for an effective cryptococcal vaccine, there are many scientific and economic challenges to overcome prior to making it a reality. Here, we discuss some of these challenges as well as steps that the community is taking for commercialization of effective cryptococcal vaccines.

Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality.

There is an urgent need to identify the cellular and molecular mechanisms responsible for severe COVID-19 that results in death. We initially performed both untargeted and targeted lipidomics as well as focused biochemical analyses of 127 plasma samples and found elevated metabolites associated with secreted phospholipase A2 (sPLA2) activity and mitochondrial dysfunction in patients with severe COVID-19. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA2 group IIA (sPLA2-IIA), with a median value that was 9.6-fold higher than that for patients with mild disease and 5.0-fold higher than the median value for survivors of severe COVID-19. Elevated sPLA2-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA2-IIA levels as a central node in the stratification of patients who died from COVID-19. Random forest analysis and least absolute shrinkage and selection operator-based (LASSO-based) regression analysis additionally identified sPLA2-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than did either one alone. An independent cohort (n = 154) confirmed higher plasma sPLA2-IIA levels in deceased patients compared with levels in plasma from patients with severe or mild COVID-19, with the PLA-BUN index-based decision tree satisfactorily stratifying patients with mild, severe, or fatal COVID-19. With clinically tested inhibitors available, this study identifies sPLA2-IIA as a therapeutic target to reduce COVID-19 mortality.