Applied Microbiology and Biotechnology

, Volume 72, Issue 2, pp 393–400 | Cite as

TGGE analysis of the diversity of ammonia-oxidizing and denitrifying bacteria in submerged filter biofilms for the treatment of urban wastewater

  • B. Gómez-Villalba
  • C. Calvo
  • R. Vilchez
  • J. González-LópezEmail author
  • B. Rodelas
Environmental Biotechnology


The spatial and temporal diversity of the bacterial community-forming biofilms in a pilot-scale submerged biofilter used for the treatment of urban wastewater was analyzed by a temperature-gradient gel electrophoresis (TGGE) approach. TGGE profiles based on partial sequence of the 16S rRNA gene showed that the community composition of the biofilms remained fairly stable along the column system and during the whole time of operation of the biofilter (more than 1 year). Community-profiling based on the amplification and separation of partial ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ) genes demonstrated that ammonia-oxidizing and denitrifying bacteria coexisted in both the anoxic and the aerated parts of the system. Several amoA and nosZ bands separated by TGGE were reamplified and sequenced, in order to further analyze the composition of these microbial communities in the biofilm. Phylogeny inferred from amoA/AmoA revealed the prevalence of Nitrosomonas species with five sequences affiliated to Nitrosomonas oligotropha, six sequences affiliated to Nitrosomonas europaea, and three sequences that showed only 75.7–76.1% identity of the DNA sequence with the closest described species (Nitrosomonas nitrosa). According to literature, this low identity value is indicative of previously undiscovered species. Eighteen new partial nosZ sequences were obtained which were mostly related to nosZ of gamma-proteobacteria (Pseudomonas) or clustered in the periphery of previously known denitrifying alpha-proteobacteria (Bradyrhizobium and Azospirillum).


Chemical Oxygen Demand Ammonia Oxidizer Azospirillum Denitrify Bacterium nosZ Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by Programa Nacional de I+D, Ministerio de Ciencia y Tecnología (MCYT), Spain (AMB99-0666-C02-01). Work by B. Gómez-Villalba was supported by an FPI Ph.D. grant (MCYT, Spain). Work by B. Rodelas was funded by Programa Ramón y Cajal (MCYT, Spain).


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • B. Gómez-Villalba
    • 1
  • C. Calvo
    • 1
    • 2
  • R. Vilchez
    • 1
  • J. González-López
    • 1
    • 2
    Email author
  • B. Rodelas
    • 1
    • 2
  1. 1.Instituto del AguaUniversidad de GranadaGranadaSpain
  2. 2.Departamento de Microbiología, Facultad de FarmaciaUniversidad de GranadaGranadaSpain

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