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Determination of metabolic alterations and therapeutic potential of Zika Virus in cancer cells by mass spectrometry and artificial intelligence

Grant number: 19/05718-3
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): December 01, 2019
Status:Discontinued
Field of knowledge:Health Sciences - Medicine - Pathological Anatomy and Clinical Pathology
Principal researcher:Rodrigo Ramos Catharino
Grantee:Jeany Delafiori
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Zika virus (ZIKV) is a flavivirus transmitted by Aedes Aegypti mosquito, which led to a high incidence of cases in Brazil between 2015 and 2016. Although 80% of the cases are asymptomatic, the later association of the disease with neurological complications such as Guillain-Barré syndrome in adults and microcephaly in neonates of infected pregnant women, led to initiatives in the development of diagnostic methods and vaccines, and studies of viral infection process in human and mosquito cells. In parallel, metabolomic strategies associated with mass spectrometry and artificial intelligence have advanced in the research of new therapeutic and diagnostic targets, with more precise and sensitive methodologies. These tools supported advances in ZIKV research, bringing innovation to the elucidation of viral infection biomarkers, understanding of virus mechanisms of action, diagnostic methods and evidences of ZIKV oncolytic potential. The Zika virus tropism for different cell types, including neural and gonads cells, associated with its ability to induce glioblastoma cell death have suggested the possibility of using ZIKV for the treatment of other cancers, such as prostate and ovary cancers. Using in vitro models, the objective is to investigate the therapeutic potential and the metabolic alterations induced by ZIKV through metabolomic studies using mass spectrometry and artificial intelligence. (AU)

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Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE ARAUJO, FABIO FERNANDES; FARIAS, DAVID DE PAULO; NERI-NUMA, IRAMAIA ANGELICA; DIAS-AUDIBERT, FLAVIA LUISA; DELAFIORI, JEANY; DE SOUZA, FLORISVALDO GAMA; CATHARINO, RODRIGO RAMOS; DO SACRAMENTO, CELIO KERSUL; PASTORE, GLAUCIA MARIA. Influence of high-intensity ultrasound on color, chemical composition and antioxidant properties of araca-boi pulp. Food Chemistry, v. 338, FEB 15 2021. Web of Science Citations: 1.
DE ARAUJO, FABIO FERNANDES; FARIAS, DAVID DE PAULO; NERI-NUMA, IRAMAIA ANGELICA; DIAS-AUDIBERT, FLAVIA LUISA; DELAFIORI, JEANY; DE SOUZA, FLORISVALDO GAMA; CATHARINO, RODRIGO RAMOS; DO SACRAMENTO, CELIO KERSUL; PASTORE, GLAUCIA MARIA. Gastrointestinal bioaccessibility and bioactivity of phenolic compounds from araca-boi fruit. LWT-FOOD SCIENCE AND TECHNOLOGY, v. 135, JAN 2021. Web of Science Citations: 0.
FARIAS, DAVID DE PAULO; DE ARAUJO, FABIO FERNANDES; NERI-NUMA, IRAMAIA ANGELICA; DIAS-AUDIBERT, FLAVIA LUISA; DELAFIORI, JEANY; CATHARINO, RODRIGO RAMOS; PASTORE, GLAUCIA MARIA. Distribution of nutrients and functional potential in fractions of Eugenia pyriformis: An underutilized native Brazilian fruit. Food Research International, v. 137, NOV 2020. Web of Science Citations: 2.
FREIRE, LUISA; BRAGA, PATRICIA A. C.; FURTADO, MARIANNA M.; DELAFIORI, JEANY; DIAS-AUDIBERT, FLAVIA L.; PEREIRA, GIULIANO E.; REYES, FELIX G.; CATHARINO, RODRIGO R.; SANT'ANA, ANDERSON S. From grape to wine: Fate of ochratoxin A during red, rose, and white winemaking process and the presence of ochratoxin derivatives in the final products. FOOD CONTROL, v. 113, JUL 2020. Web of Science Citations: 0.
DIAS-AUDIBERT, FLAVIA LUISA; NAVARRO, LUIZ CLAUDIO; DE OLIVEIRA, DIOGO NOIN; DELAFIORI, JEANY; MELO, CARLOS FERNANDO ODIR RODRIGUES; GUERREIRO, TATIANE MELINA; ROSA, FLAVIA TRONCON; PETENUCI, DIEGO LIMA; WATANABE, MARIA ANGELICA EHARA; VELLOSO, LICIO AUGUSTO; ROCHA, ANDERSON REZENDE; CATHARINO, RODRIGO RAMOS. Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, v. 8, JAN 24 2020. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.