Immunological assays based on the detection of circulating fungal biomarkers are helpful in clinical practice for diagnosing invasive fungal infections. Some of these targeting antigenic components are common to several different fungi. Histoplasmosis is a mycosis caused by the dimorphic fungus Histoplasma capsulatum, which in recent years has gained significant relevance due to the increase in the population susceptible to developing severe clinical forms, including those living with HIV/AIDS. Immunological tests that detect cell wall polysaccharide antigens are among the most used laboratory techniques for diagnosing this mycosis. However, none have shown adequate performance, and cross-reactivity with other fungal pathogens may be observed. Considering that there is a real need to improve the sensitivity and specificity of current diagnostic methods, we explored a novel strategy for the identification of H. capsulatum-specific antigens (Hc_Ags) that could be detected in clinical samples during infection based on a computational analysis model that included the application of bioinformatics tools and the analysis of experimental data from transcriptomics and proteomics. The Hc_Ags identified were expressed and purified by eukaryotic and prokaryotic systems. First, the Hc_Ags were used in an in-house immunization model in mice (BALB/c) to obtain Hc_Ag-specific polyclonal antibodies (Hc_Ag_PAb). Then, the presence of these antigens in H. capsulatum-yeast culture extracts and the specificity of Hc_Ag_PAb against culture extracts of Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Fusarium spp., and Paracoccidioides brasiliensis were confirmed. Finally, we demonstrated the immunoreactivity of these Hc_Ag-specific polyclonal antibodies with urine samples from patients previously diagnosed with histoplasmosis.IMPORTANCEHistoplasmosis is considered one of the most important mycoses due to the increasing number of individuals susceptible to develop severe clinical forms, particularly those with HIV/AIDS or receiving immunosuppressive biological therapies, the high mortality rates reported when antifungal treatment is not initiated in a timely manner, and the limitations of conventional diagnostic methods. In this context, there is a clear need to improve the capacity of diagnostic tools to specifically detect the fungal pathogen, regardless of the patient's clinical condition or the presence of other co-infections. The proposed novel pathogen-specific biomarkers have the potential to be used in immunodiagnostic platforms and antifungal treatment monitoring in histoplasmosis. In addition, the bioinformatics strategy used in this study could be applied to identify potential diagnostic biomarkers in other models of fungal infection of public health importance.
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