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Quantitative assessment of phenol hydroxylase diversity in bioreactors using a functional gene analysis

  • Environmental Biotechnology
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Abstract

We describe a quantitative analysis of the genetic diversity of phenol-degrading potential in bacterial communities from laboratory-scale activated sludge. Genomic DNA extracted from activated sludge from two sequential batch reactors fed with synthetic sewage plus phenol was amplified using conserved primers for the major subunit of the phenol hydroxylase (LmPH) gene and used to generate clone libraries. Following phylogenetic analysis, 59 sequences containing a 470-bp fragment clustered into six distinct subgroups with a genetic distance of 8%, most likely representing ecologically relevant variants of the enzyme. Seven sets of primers were designed to target the six clusters and used to obtain quantitative information on the dynamics of LmPH gene diversity using real-time PCR assays throughout 9 months of bioreactors operation. Total LmPH gene copy number remained approximately steady in phenol-amended and control reactors. However, a significant increase in phenol-degrading activity in the phenol-amended sludge was accompanied by a parallel increase in LmPH gene diversity, suggesting that phenol degradation in the activated sludge depends on the combined activity of a number of redundant species.

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Acknowledgments

We thank one of the anonymous reviewers for valuable comments that helped us to improve the presentation of the manuscript. This work was supported by FONCyT (PICT 2003, No. 01-14218) and CONICET (PIP 5548). L.A.B. is a fellow, and L. E. is a career investigator, of CONICET.

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  1. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Vuelta de Obligado 2490, ADN1428, Buenos Aires, Argentina

    Laura A. Basile & Leonardo Erijman

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  1. Laura A. Basile
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  2. Leonardo Erijman
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Correspondence to Leonardo Erijman.

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Basile, L.A., Erijman, L. Quantitative assessment of phenol hydroxylase diversity in bioreactors using a functional gene analysis. Appl Microbiol Biotechnol 78, 863–872 (2008). https://doi.org/10.1007/s00253-008-1351-3

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  • DOI: https://doi.org/10.1007/s00253-008-1351-3

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