Comparative Cross-Kingdom DDA- and DIA-PASEF Proteomic Profiling Reveals Novel Determinants of Fungal Virulence and a Putative Druggable Target.

Abstract

Accurate and reliable detection of fungal pathogens presents an important hurdle to manage infections, especially considering that fungal pathogens, including the globally important human pathogen, Cryptococcus neoformans, have adapted diverse mechanisms to survive the hostile host environment and moderate virulence determinant production during coinfections. These pathogen adaptations present an opportunity for improvements (e.g., technological and computational) to better understand the interplay between a host and a pathogen during disease to uncover new strategies to overcome infection. In this study, we performed comparative proteomic profiling of an in vitro coinfection model across a range of fungal and bacterial burden loads in macrophages. Comparing data-dependent acquisition and data-independent acquisition enabled with parallel accumulation serial fragmentation technology, we quantified changes in dual-perspective proteome remodeling. We report enhanced and novel detection of pathogen proteins with data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF), especially for fungal proteins during single and dual infection of macrophages. Further characterization of a fungal protein detected only with DIA-PASEF uncovered a novel determinant of fungal virulence, including altered capsule and melanin production, thermotolerance, and macrophage infectivity, supporting proteomics advances for the discovery of a novel putative druggable target to suppress C. neoformans pathogenicity.