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Characterization of aerobic polycyclic aromatic hydrocarbon-degrading bacteria from Bizerte lagoon sediments, Tunisia
To characterize polycyclic aromatic hydrocarbon (PAH)-degrading bacteria from sediments of the Bizerte lagoon, and to determine their ability to resist other pollutants such as antibiotics and heavy metals. More than 100 strains were isolated for their ability to use fluoranthene as the sole carbon...
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Published in: | Journal of applied microbiology 2008-04, Vol.104 (4), p.987-997 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | To characterize polycyclic aromatic hydrocarbon (PAH)-degrading bacteria from sediments of the Bizerte lagoon, and to determine their ability to resist other pollutants such as antibiotics and heavy metals. More than 100 strains were isolated for their ability to use fluoranthene as the sole carbon and energy source. Most of them showed antibiotic and heavy metal resistance; 20 representative strains were selected for further analysis. 16S rRNA coding sequences analysis showed that the majority of the selected bacteria (75%) were affiliated to the Gammaproteobacteria. The selected strains also utilized high molecular weight PAHs containing up to four benzene rings and showed different profiles of PAH substrate usage suggesting different PAH degradation pathways. These results are consistent with the fact that nah-like genes and idoA-like genes, involved in PAH degradation, were detected in 6 and 1 strains respectively. The Bizerte lagoon, polluted by many human activities, leads to the co-selection of strains able to cope with multiple contaminants. Polluted areas are often characterized by the concomitant presence of organic pollutants, heavy metals and antibiotics. This study is one of the first showing bacterial strains adapted to multiple contaminants, a promising potential for the development of bioremediation processes. |
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ISSN: | 1364-5072 1365-2672 |
DOI: | 10.1111/j.1365-2672.2007.03621.x |