Microbial Ecology

, Volume 49, Issue 3, pp 407–415 | Cite as

Characterization of Bacterial Community Structure in Rhizosphere Soil of Grain Legumes

  • S. SharmaEmail author
  • M.K. Aneja
  • J. Mayer
  • J.C. Munch
  • M. Schloter


Molecular techniques were used to characterize bacterial community structure, diversity (16S rDNA), and activity (16S rRNA) in rhizospheres of three grain legumes: faba beans (Vicia faba L., cv. Scirocco), peas (Pisum sativum L., cv. Duel) and white lupin (Lupinus albus L., cv. Amiga). All plants were grown in the same soil under controlled conditions in a greenhouse and sampled after fruiting. Amplified 16S rDNA and rRNA products (using universal bacterial primers) were resolved by denaturing gradient gel electrophoresis (DGGE). Distinct profiles were observed for the three legumes with most of the bands derived from RNA being a subset of those derived from DNA. Comparing the total bacterial profiles with actinomycete-specific ones (using actinomycete-specific primers) highlighted the dominance of this group in the three rhizospheres. 16S PCR and RT-PCR products were cloned to construct libraries and 100 clones from each library were sequenced. Actinomycetes and proteobacteria dominated the clone libraries with differences in the groups of proteobacteria. Absence of β-subdivision members in pea and γ-subdivision members of proteobacteria in faba bean rhizosphere was observed. Plant-dependent rhizosphere effects were evident from significant differences in the bacterial community structure of the legume rhizospheres under study. The study gives a detailed picture of both residing and „active” bacterial community in the three rhizospheres. The high abundance of actinomycetes in the rhizospheres of mature legumes indicates their possible role in soil enrichment after the legumes are plowed into the soil as biofertilizers.


Bacterial Community Clone Library Proteobacteria Rhizosphere Soil Faba Bean 
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.



The study was supported by a research grant MU 831/10-1 from the Deutsche Forschungsgemeinschaft (DFG), Bonn, Germany.


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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • S. Sharma
    • 1
    Email author
  • M.K. Aneja
    • 1
  • J. Mayer
    • 2
  • J.C. Munch
    • 1
  • M. Schloter
    • 1
  1. 1.Institute of Soil EcologyGSF — National Research Center for Environment and HealthNeuherbergGermany
  2. 2.Department of Organic Farming and Cropping SystemsUniversity of KasselWitzenhausenGermany

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