Engineering Nodulation Competitiveness of Rhizobial Bioinoculants in Soils

  • Gattupalli ArchanaEmail author


In field conditions, inoculated strains of rhizobia are at a survival disadvantage as compared to indigenous strains that are well adapted to local environment. Consequently, nodulation by unwanted strains is a major problem in enhancement of legume growth by rhizobial bio-inoculants. Competitiveness determinants include motility, chemotaxis, cell surface components, ability to use certain substrates, storage polymers, and production of antimicrobial compounds, higher growth rates, and ability to bring about faster infection. More recently, the involvement of other factors such as quorum sensing, the ability to form biofilms, and presence of protein secretion machinery has been shown to be important. Using genomics-based approach, numerous competitiveness genes have been identified. Variation in competitiveness traits among different legume-microsymbionts is becoming apparent. Approaches for the development of competitive bioinoculants by genetic engineering employ the following strategies (a) production of antimetabolites to inhibit nodule occupancy of native rhizobia, (b) interference with the regulation of plant–microbe signaling molecules to ensure efficient nodulation, (c) specific adaptation of the inoculated strain to environmental stresses, and (d) improved nutrition of the inoculant strain for competitive sustenance in soil or rhizosphere including root-derived compounds as well as other soil metabolites such as siderophore iron complexes. Engineering rhizobia for enhanced competitiveness is a challenging aspect of developing effective bioinoculants and ability to utilize heterologous siderophores could provide them with better iron acquisition ability and consequently, rhizosphere stability.


Nitrogen Fixation Rhizobial Strain Infection Thread Inoculant Strain Nodule Occupancy 
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.



I would like to thank students from our group whose work has been cited in this chapter. I also express gratitude to Retd. Prof. A. J. Desai, M. S. University of Baroda and funding agencies DBT, India and UGC, India for financial support of the work carried out in our laboratory.


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© Springer-Verlag/Wien 2010

Authors and Affiliations

  1. 1.Department of Microbiology and Biotechnology CenterMaharaja Sayajirao University of BarodaVadodaraIndia

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