Marcadores biomoleculares e vias de sinalização em processos neoplásicos

• Ano de defesa: 2020
• Autor(a) principal: Tavares, Paula Cristina Brigido
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Imunologia e Parasitologia Aplicadas
• Palavras-chave em Português: Espectrômetro massas; Metabolômica; Hanseníase; Diagnóstico; Hansen's disease; Diagnosis; Mass spectrometer; Metabolomics; CNPQ::CIENCIAS BIOLOGICAS::GENETICA::GENETICA MOLECULAR E DE MICROORGANISMOS; CNPQ::CIENCIAS BIOLOGICAS::MICROBIOLOGIA::MICROBIOLOGIA APLICADA; Imunologia; Hanseníase - Diagnóstico; Espectrometria de massa; Metabologia
• Link de acesso: https://repositorio.ufu.br/handle/123456789/33304; http://doi.org/10.14393/ufu.te.2021.62

Hansen's disease still represents a health problem in Brazil and worldwide. This disease, caused by Mycobacterium leprae, mainly affects the skin and the peripheral nervous system. Currently, clinical symptoms are used for the diagnosis of this disease; however, there is still no fast and reliable laboratory diagnosis. The early diagnosis of Hansen’s disease and monitoring strategies for populations at risk of becoming ill is a challenge for its eradication. This study sought to identify changes in the metabolic profile induced in the serum of Paucibacillary and Multibacillary patients. In this study, plasma was used from individuals with different forms of Hansen’s disease (PB = 42, Mb = 39), as well as from healthy individuals (control group, n = 37). The analysis of the metabolomic profile was carried out in the High-Performance Liquid Chromatography (HPLC) system coupled to a quadrupole-flight-time mass spectrometer. The metabolic profile allowed the identification of 14 metabolites differentially expressed in the serum; 14 were responsible for the differences between the PB and control groups. Likewise, 14 metabolites were expressed differently between the MB and control groups; 4 differentially expressed metabolites were responsible for the difference between the PB and MB groups. Moreover, using the Random Forest classification algorithm, it was possible to differentiate the groups of infected and uninfected patients with an accuracy of 98.62%. These metabolites must be validated so that these molecules can be used as biomarkers not only to classify clinical forms but also to assess disease progression or to be used as therapeutic targets.