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Genomics and in silico prospecting of biosurfactants from petroleum industry bacteria

Grant number: 19/03566-1
Support type:Scholarships in Brazil - Master
Effective date (Start): July 01, 2019
Effective date (End): June 30, 2021
Field of knowledge:Biological Sciences - Genetics
Principal Investigator:Valeria Maia Merzel
Grantee:Carla Thais Moreira Paixão
Home Institution: Centro Pluridisciplinar de Pesquisas Químicas, Biológicas e Agrícolas (CPQBA). Universidade Estadual de Campinas (UNICAMP). Paulínia , SP, Brazil

Abstract

Seeking new alternatives that can effectively replace the great demand for synthetic surfactants that are toxic, non-biodegradable and environmentally hazardous elements, biosurfactants are a promising alternative, presenting important advantages such as biodegradability, adaptation to adverse conditions, ecological acceptability, and they hold the possibility of being genetically modified. In addition, they currently have a wide range of biotechnological applications, such as antitumor agents, antimicrobials, antiparasitics, remediation processes for environments contaminated with petroleum residues, improved oil recovery, among others. Biosurfactants are produced by a large variety of bacteria, yeasts and filamentous fungi, and the rhamnolipids and surfactin, produced by Pseudomonas aeruginosa and Bacillus subtilis, respectively, are considered the most effective in reducing surface tension even under adverse conditions. In this context, this study aims to carry out the genomic prospection of bacteria from the Oil and Energy Research Collection (APPE), previously isolated from petroleum industry environments and processes, for the production of secondary metabolites with biotechnological application, with emphasis on biosurfactants. Bacteria will be subjected to sorting through surface tension reduction, emulsification and de-emulsification tests in order to select the best producing strains. These will have the genome sequenced through the Illumina® platform (Illumina®Sequencing), for further analysis of genome assembly and annotation of the predicted genes. The in silico search of metabolic pathways, operons and gene clusters that code for the production of secondary metabolites, with emphasis on biosurfactants, through the antiSMASH platform. The results obtained may provide subsidies for future strategies to optimize the fermentation processes and consequent reduction of the costs of production and recovery of biosurfactants.