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Propioniciclava soli sp. nov., isolated from forest soil, Yunnan, China, and reclassification of the genus Brevilactibacter into the genus Propioniciclava, and Brevilactibacter sinopodophylli, Brevilactibacter flavus, and Brevilactibacter coleopterorum as Propioniciclava sinopodophylli comb. nov., Propioniciclava flava comb. nov., and Propioniciclava coleopterorum comb. nov., respectively.

Le-Le LiJiang-Yuan ZhaoYu GengYu-Guang ZhouHui-Ren YuanMan CaiMeng-Liang WenMing-Gang LiShu-Kun Tang
Published in: Archives of microbiology (2021)
A Gram-stain-positive, coccus-shaped, facultatively anaerobic, non-motile bacterial strain, designated YIM S02567 T , was isolated from a forest soil sample collected from Gejiu City, Yunnan Province, southwest PR China. Growth was observed at 10-45 °C, at pH 6.0-9.5, in the presence of up to 4.0% (w/v) NaCl on R2A medium. The results of 16S rRNA gene sequence similarity analysis showed that strain YIM S02567 T was most closely related to the type strain of Brevilactibacter sinopodophylli (95.4%) and Propioniciclava tarda (94.7%), and phylogenetic analysis based on genome data showed that strain YIM S02567 T should be assigned to the genus Propioniciclava. The cell-wall diamino acid was meso-diaminopimelic acid. The major cellular fatty acids were identified as anteiso-C 15:0 and C 16:0 , and the major polar lipids were diphosphatidylglycerol, phosphatidylglycerol, and two unidentified glycolipids. The predominant menaquinone was MK-9(H 4 ). The genomic DNA G + C content was 71.2 mol%. Based on the polyphasic taxonomic evidence, strain YIM S02567 T is assigned to a novel member of the genus Propioniciclava, for which the name Propioniciclava soli sp. nov., (type strain YIM S02567 T  = CCTCC AB 2020128 T  = CGMCC 1.18504 T  = KCTC 49478 T ) is proposed. Furthermore, we propose the reclassification of Brevilactibacter as Propioniciclava gen. nov.
Keyphrases
  • fatty acid
  • cell wall
  • genome wide
  • copy number
  • gene expression
  • electronic health record
  • single molecule
  • deep learning
  • heavy metals