Molecular analysis of the bacterial diversity in a specialized consortium for diesel oil degradation
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Data
2010-01-01
Autores
Paixão, Douglas Antonio Alvaredo [UNESP]
Dimitrov, Mauricio Rocha
Pereira, Rodrigo Matheus
Accorsini, Fábio Raphael [UNESP]
Vidotti, Maria Benincasa [UNESP]
de Macedo Lemos, Eliana Gertrudes [UNESP]
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Resumo
Diesel oil is a compound derived from petroleum, consisting primarily of hydrocarbons. Poor conditions in transportation and storage of this product can contribute significantly to accidental spills causing serious ecological problems in soil and water and affecting the diversity of the microbial environment. The cloning and sequencing of the 16S rRNA gene is one of the molecular techniques that allows estimation and comparison of the microbial diversity in different environmental samples. The aim of this work was to estimate the diversity of microorganisms from the Bacteria domain in a consortium specialized in diesel oil degradation through partial sequencing of the 16S rRNA gene. After the extraction of DNA metagenomics, the material was amplified by PCR reaction using specific oligonucleotide primers for the 16S rRNA gene. The PCR products were cloned into a pGEM-T-Easy vector (Promega), and Escherichia coli was used as the host cell for recombinant DNAs. The partial clone sequencing was obtained using universal oligonucleotide primers from the vector. The genetic library obtained generated 431 clones. All the sequenced clones presented similarity to phylum Proteobacteria, with Gammaproteobacteria the most present group (49.8 % of the clones), followed by Alphaproteobacteira (44.8 %) and Betaproteobacteria (5.4 %). The Pseudomonas genus was the most abundant in the metagenomic library, followed by the Parvibaculum and the Sphingobium genus, respectively. After partial sequencing of the 16S rRNA, the diversity of the bacterial consortium was estimated using DOTUR software. When comparing these sequences to the database from the National Center for Biotechnology Information (NCBI), a strong correlation was found between the data generated by the software used and the data deposited in NCBI.
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16s rRNA, Biosurfactants, Cloning, Metagenomics, PCR, Soil
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Revista Brasileira de Ciencia do Solo, v. 34, n. 3, p. 773-781, 2010.