Proteomics-based identification of novel Candida albicans antigens for diagnosis of systemic candidiasis in patients with underlying hematological malignancies.

Systemic candidiasis remains a major cause of disease and death, particularly among patients suffering from hematological malignancies. In an attempt to contribute to the discovery of useful biomarkers for its diagnosis and therapeutic monitoring, we embarked on a mapping of Candida albicans immunogenic proteins specifically recognized by antibodies produced during the natural course of systemic Candida infection in this high-risk population. About 85 immunoreactive protein species were detected with systemic candidiasis patients' serum specimens by using immunoproteomics (i.e., two-dimensional electrophoresis followed by Western blotting), and identified through a combination of peptide mass fingerprinting by matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS), de novo peptide sequencing using nano-electrospray ionization-ion trap (ESI-IT) MS, and genomic database searches. This proteomic approach has led to the characterization of 42 different housekeeping enzymes as C. albicans antigens. Their biological significance is also discussed. Furthermore, this study is the first to report that 26 of them exhibit antigenic properties in C. albicans, and 35 of them become targets of the human antibody response to systemic candidiasis. Our findings suggest that the production of antibodies to C. albicans phosphoglycerate kinase and alcohol dehydrogenase during systemic candidiasis could be associated with a differentiation of the human immune response. We also highlight the relationship between changes in maintenance of circulating levels of specific anti-Candida antibodies and patients' outcome. Some of these variations, especially the rise of high anti-enolase antibody concentrations, appear to be related to recovery from systemic candidiasis in these patients, which might serve as markers for predicting their outcome. This approach could therefore provide new challenges for diagnosis and clinical follow-up of these fungal infections, and even for antifungal drug or vaccine design.