Abstract

Poster session 3, September 23, 2022, 12:30 PM - 1:30 PMObjective: To identify and produce novel biomarkers with potential use for the specific diagnosis of H. capsulatum infection.MethodsHere, we design a novel strategy to search and select new Candidate genes for biomarkers that integrates the use of a computational analysis model that includes the application of bioinformatic tools such as OrthoMCL, BLASTp, TargetP, and SignalP, applied on a local collection of proteome database obtained manually from GenBank-NCBI, and the analysis of previously published biological and experimental data sets, including a secreted proteome database obtained from pathogenic yeast-phase H. capsulatum culture filtrates, a Histoplasma yeast and mycelial transcriptomes database, and a urine-peptides database from Histoplasma-immunoassay-positive patients.For the synthesis of the Candidates, an internal protocol for the production of recombinant proteins in prokaryotic and eukaryotic systems was applied. Obtaining polyclonal antibodies (PAb) specific for each biomarker was carried out by adapting a rapid immunization protocol for BALB/c mice.Finally, the computational model was experimentally validated, evaluating the reactivity and specificity of PAb anti-Histoplasma with fungus culture extracts and samples from patients with histoplasmosis.ResultsUsing the computational analysis model, 2 Candidate genes for diagnostic biomarkers were identified. Subsequently, the construction of expression vector for each Candidate and the production of these genes were achieved using a standardized protocol for the production of recombinant proteins.Polyclonal antibodies (PAb) anti-histoplasma were obtained and shown to be reactive against purified H. capsulatum-antigens. Finally, we confirmed the presence of these antigens in yeast culture extracts of H. capsulatum and demonstrated the immunoreactivity of anti-Histoplasma PAb with urine samples from patients previously diagnosed with histoplasmosis.ConclusionThe generation of novel strategies that combine data analysis, computational tools, and transcriptomic and proteomic techniques could be very useful for the identification of new biomarker genes and the development of microbiological diagnostic tests for important pathogens.

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