Login / Signup

Using amplicon sequencing of rpoB for identification of inoculant rhizobia from peanut nodules.

S A MousaviY GaoPenttinen PetriÅ FrostegårdL PaulinK Lindström
Published in: Letters in applied microbiology (2021)
To improve the nitrogen fixation, legume crops are often inoculated with selected effective rhizobia. However, there is large variation in how well the inoculant strains compete with the indigenous microflora in soil. To assess the success of the inoculant, it is necessary to distinguish it from other, closely related strains. Methods used until now have generally been based either on fingerprinting methods or on the use of reporter genes. Nevertheless, these methods have their shortcomings, either because they do not provide sufficiently specific information on the identity of the inoculant strain, or because they use genetically modified organisms that need prior authorization to be applied in the field or other uncontained environments. Another possibility is to target a gene that is naturally present in the bacterial genomes. Here we have developed a method that is based on amplicon sequencing of the bacterial housekeeping gene rpoB, encoding the beta-subunit of the RNA polymerase, which has been proposed as an alternative to the 16S rRNA gene to study the diversity of rhizobial populations in soils. We evaluated the method under laboratory and field conditions. Peanut seeds were inoculated with various Bradyrhizobium strains. After nodule development, DNA was extracted from selected nodules and the nodulating rhizobia were analysed by amplicon sequencing of the rpoB gene. The analyses of the sequence data showed that the method reliably identified bradyrhizobial strains in nodules, at least at the species level, and could be used to assess the competitiveness of the inoculant compared to other bradyrhizobia.
Keyphrases
  • genome wide
  • escherichia coli
  • genome wide identification
  • copy number
  • single cell
  • dna methylation
  • gene expression
  • crispr cas
  • multidrug resistant
  • big data
  • genetic diversity
  • human health
  • drug induced