Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis

Texto completo
Autor(es):
Neto, Fausto Carnevale [1, 2] ; Pilon, Alan C. [2] ; Selegato, Denise M. [2] ; Freire, Rafael T. [2, 3] ; Gu, Haiwei [4, 5] ; Raftery, Daniel [4, 6] ; Lopes, Norberto P. [1] ; Castro-Gamboa, Ian [2]
Número total de Autores: 8
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Fac Ciencias Farmaceut Ribeirao Preto, Dept Fis & Quim, Nucleo Pesquisas Prod Nat & Sintet, Ribeirao Preto - Brazil
[2] Univ Estadual Paulista UNESP, Inst Quim, Dept Quim Organ, Nulcleo Bioensaios Biossintese & Ecofisiol Prod N, Araraquara - Brazil
[3] Univ Sao Paulo, Inst Fis Sao Carlos, Ctr Imagens & Espectroscopia In Vivo Ressonancia, Sao Carlos, SP - Brazil
[4] Univ Washington, Northwest Metabol Res Ctr, Dept Anesthesiol & Pain Med, Seattle, WA 98195 - USA
[5] East China Inst Technol, Jiangxi Key Lab Mass Spectrometry & Instrumentat, Nanchang, Jiangxi - Peoples R China
[6] Fred Hutchinson Canc Res Ctr, Publ Hlth Sci Div, 1124 Columbia St, Seattle, WA 98104 - USA
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: FRONTIERS IN MOLECULAR BIOSCIENCES; v. 3, 2016.
Citações Web of Science: 7
Resumo

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts. (AU)

Processo FAPESP: 03/02176-7 - Conservação e uso sustentável da diversidade do Cerrado e da Mata Atlântica: diversidade química e prospecção de medicamentos potenciais - fase II
Beneficiário:Vanderlan da Silva Bolzani
Linha de fomento: Auxílio à Pesquisa - Programa BIOTA - Temático
Processo FAPESP: 10/17935-4 - Desenvolvimento de métodos analíticos de desreplicação por RMN e análise multivariada do perfil metabolômico de espécies de Solanaceae com potencial biológico
Beneficiário:Alan Cesar Pilon
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 14/05935-0 - Co-cultura de micro-organismos isolados da rizosfera de Senna spectabilis visando a produção de metabólitos bioativos
Beneficiário:Denise Medeiros Selegato
Linha de fomento: Bolsas no Brasil - Doutorado Direto