ABSTRACT: Fungal diseases impact the lives of millions of people across the globe ranging from superficial to systemic infections. Treatment options toward fungal diseases are limited given the emergence of new pathogens with intrinsic resistance and heightened evolution toward resistant strains. To effectively combat fungal disease, rapid and reliable diagnostic methods are required, including current methodologies using antigen detection, culturing, microscopy, and molecular tools. However, the flexibility of these platforms to diagnose infection using non-invasive methods and predict the outcome of disease are limited. In this study, we apply state-of-the-art mass spectrometry-based proteomics to perform dual perspective (i.e., host and pathogen) profiling of cryptococcal infection. Whole blood collected over a temporal scale following murine model challenged with the human fungal pathogen, Cryptococcus neoformans, revealed detection of >3,000 host proteins and 160 fungal proteins across all samples. From the host perspective, temporal regulation of known immune-associated proteins, including eosinophil peroxidase and lipocalin, along with suppression of lipoproteins, demonstrated infection- and time-dependent host remodeling. Conversely, from the pathogen perspective, known and putative virulence-associated proteins were detected, including proteins associated with fungal extracellular vesicles and host immune modulation. Moreover, we observed and validated a new mechanism of immune system activation by C. neoformans through modulation of haptoglobin. Further, we assessed the predictive power of dual perspective proteome profiling toward prognostics of cryptococcal infection and report a previously undisclosed integration among virulence factor production, immune system modulation, and individual model survival. Together, our findings pose novel biomarkers of cryptococcal infection from whole blood and highlight the potential of personal proteome profiles to determine the prognosis of cryptococcal infection, a new parameter in fungal disease management. his submission includes a partial dataset. Additional RAW files will be added shortly after manuscript publication to complete the full data submission.
Jennifer Geddes-McAlister 
