Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

Author(s):Show less -

Lima, Estela de Oliveira ^{[1, 2]} ; Navarro, Luiz Claudio ^[3] ; Morishita, Karen Noda ^[2] ; Kamikawa, Camila Mika ^[4] ; Martins Rodrigues, Rafael Gustavo ^[2] ; Dabaja, Mohamed Ziad ^[2] ; de Oliveira, Diogo Noin ^[2] ; Delahori, Jeany ^[2] ; Dias-Audibert, Flavia Luisa ^[2] ; Ribeiro, Marta da Silva ^[2] ; Vicentini, Adriana Pardini ^[4] ; Rocha, Anderson ^[3] ; Catharino, Rodrigo Ramos ^[2]

Affiliation

^[1] Sao Paulo State Univ, Botucatu Med Sch, Dept Internal Med, Botucatu, SP - Brazil

^[2] Univ Estadual Campinas, Sch Pharmaceut Sci, INNOVARE Biomarkers Lab, Campinas, SP - Brazil

^[3] Univ Estadual Campinas, Inst Comp, RECOD Lab, Campinas, SP - Brazil

^[4] Adolfo Lutz Inst, Lab Mycosis Immunodiag, Immunol Sect, Sao Paulo, SP - Brazil

• Document type: Journal article
• Source: MSYSTEMS; v. 5, n. 3 MAY-JUN 2020.
• Web of Science Citations: 5

Abstract

Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients' blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients' condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors. (AU)

• FAPESP's process: 19/05718-3 - Determination of metabolic alterations and therapeutic potential of Zika Virus in cancer cells by mass spectrometry and artificial intelligence
• Grantee: Jeany Delafiori
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)