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The outstanding diversity of rhizobia microsymbionts of common bean (Phaseolus vulgaris L.) in Mato Grosso do Sul, central-western Brazil, revealing new Rhizobium species.

Fernanda Terezinha MouraLuisa Caroline Ferraz HeleneRenan Augusto RibeiroMarco Antonio NogueiraMariangela Hungria
Published in: Archives of microbiology (2023)
Common bean is considered a legume of great socioeconomic importance, capable of establishing symbioses with a wide variety of rhizobial species. However, the legume has also been recognized for its low efficiency in fixing atmospheric nitrogen. Brazil is a hotspot of biodiversity, and in a previous study, we identified 13 strains isolated from common bean (Phaseolus vulgaris) nodules in three biomes of Mato Grosso do Sul state, central-western Brazil, that might represent new phylogenetic groups, deserving further polyphasic characterization. The phylogenetic tree of the 16S rRNA gene split the 13 strains into two large clades, seven in the R. etli and six in the R. tropici clade. The MLSA with four housekeeping genes (glnII, gyrB, recA, and rpoA) confirmed the phylogenetic allocation. Genomic comparisons indicated eight strains in five putative new species and the remaining five as R. phaseoli. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) comparing the putative new species and the closest neighbors ranged from 81.84 to 92.50% and 24.0 to 50.7%, respectively. Other phenotypic, genotypic, and symbiotic features were evaluated. Interestingly, some strains of both R. etli and R. tropici clades lost their nodulation capacity. The data support the description of the new species Rhizobium cerradonense sp. nov. (CNPSo 3464 T ), Rhizobium atlanticum sp. nov. (CNPSo 3490 T ), Rhizobium aureum sp. nov. (CNPSo 3968 T ), Rhizobium pantanalense sp. nov. (CNPSo 4039 T ), and Rhizobium centroccidentale sp. nov. (CNPSo 4062 T ).
Keyphrases
  • escherichia coli
  • single molecule
  • circulating tumor
  • genome wide
  • copy number
  • south africa
  • cell free
  • gene expression
  • machine learning
  • dna methylation
  • nucleic acid
  • particulate matter
  • deep learning