JOURNAL ARTICLE

Characterization of rhizobia for beneficial traits that promote nodulation in legumes under abiotically stressed conditions.

  • Published In: Letters in Applied Microbiology, 2023, v. 76, n. 9. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Khambani, Langutani Sanger; Hassen, Ahmed Idris; Rumbold, Karl 3 of 3

Abstract

This article focuses on the phenotypic and phylogenetic characterization of 40 rhizobia strains isolated from root nodules of indigenous and exotic legumes in South Africa and other countries, with an emphasis on their tolerance to abiotic stresses relevant to sustainable agriculture. Using multilocus sequence analysis of genes including 16S rRNA, recA, nodA, nodC, acdS, and exoR, the isolates were identified as belonging to the genera Sinorhizobium, Bradyrhizobium, Rhizobium, Mesorhizobium, and Aminobacter, exhibiting significant variation in tolerance to temperature, salinity, acidity/alkalinity, and heavy metals. The study highlights that several isolates, particularly those nodulating Vigna unguiculata, showed strong tolerance to multiple abiotic stresses and possessed beneficial genes such as acdS (encoding ACC deaminase) and exoR (involved in exopolysaccharide synthesis), which are linked to stress mitigation and effective nodulation. These findings provide baseline molecular and phenotypic data to support the selection and development of elite rhizobia inoculants for legume production under climate-affected and stress-prone environments, especially in sub-Saharan Africa.

Additional Information

  • Source:Letters in Applied Microbiology. 2023/09, Vol. 76, Issue 9, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0266-8254
  • DOI:10.1093/lambio/ovad106
  • Accession Number:173516986
  • Copyright Statement:Copyright of Letters in Applied Microbiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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