ESTUDIO DEL METABOLOMA EN EL ANTAGONISMO MICROBIANO A TRAVÉS DE CROMATOGRAFÍA LÍQUIDA CON ALGORITMOS QUIMIOMÉTRICOS

B. Chalén-Alvarado; C. Quiroz-Moreno; NGS. Mogollón; C. Domínguez; J. Rodríguez

doi:10.47187/perf.v2i22.50

Authors

B. Chalén-Alvarado Escuela Superior Politécnica del Litoral, ESPOL, (Centro Nacional de Acuicultura e Investigaciones Marinas - CENAIM), Guayaquil, Ecuador
C. Quiroz-Moreno Universidad Regional Amazónica Ikiam, Tena, Napo, Ecuador
NGS. Mogollón Universidad Regional Amazónica Ikiam, Tena, Napo, Ecuador
C. Domínguez Escuela Superior Politécnica del Litoral, ESPOL, (Centro Nacional de Acuicultura e Investigaciones Marinas - CENAIM), Guayaquil, Ecuador
J. Rodríguez Escuela Superior Politécnica del Litoral, ESPOL, (Centro Nacional de Acuicultura e Investigaciones Marinas - CENAIM), Guayaquil, Ecuador

DOI:

https://doi.org/10.47187/perf.v2i22.50

Keywords:

Microbial antagonism, liquid chromatography, principal component analysis, Pseudovibrio denitrificans, Vibrio harveyi

Abstract

Metabolome is a group of low molecular weight organic compounds (metabolites) produced by biological systems. Bacterial antagonism is an important evolving force, in this sense analysis of its metabolome represents a useful tool for discovering new molecules with biological activity. The objective of this research work was to implement the use of chemometric algorithms for the identification of variations in the metabolome of the microbial antagonism between Pseudovibrio denitrificans and Vibrio harveyi. Extracts from cultured bacteria and used culture media were analyzed by Ultra-high performance liquid chromatography coupled with a diode-array detector (UHPLC-DAD). Additionally, chemometric algorithms were employed, subjecting to Principal Component Analysis (PCA) for the obtained chromatograms. Three peaks were found that express the major variability between the individual metabolome and the metabolome from the interaction of P. denitrificans and V. harveyi. In this manner, metabolomic through UHPLC- DAD and chemometric algorithms, showed to be a useful tool to identify the peaks responsible for differences between microbial interactions.

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