Strain identifier

BacDive ID: 133736

Type strain: Yes

Species: Skermanella stibiiresistens

Strain Designation: SB22

NCBI tax ID(s): 913326 (species)

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General

@ref: 30338

BacDive-ID: 133736

keywords: genome sequence, 16S sequence, Bacteria, aerobe, Gram-negative, motile, rod-shaped

description: Skermanella stibiiresistens SB22 is an aerobe, Gram-negative, motile bacterium that was isolated from soil.

NCBI tax id

  • NCBI tax id: 913326
  • Matching level: species

doi: 10.13145/bacdive133736.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/pseudomonadota
  • domain: Bacteria
  • phylum: Pseudomonadota
  • class: Alphaproteobacteria
  • order: Rhodospirillales
  • family: Azospirillaceae
  • genus: Skermanella
  • species: Skermanella stibiiresistens
  • full scientific name: Skermanella stibiiresistens Luo et al. 2012

@ref: 30338

domain: Bacteria

phylum: Proteobacteria

class: Alphaproteobacteria

order: Rhodospirillales

family: Rhodospirillaceae

genus: Skermanella

species: Skermanella stibiiresistens

strain designation: SB22

type strain: yes

Morphology

cell morphology

@refgram staincell shapemotilityconfidence
30338negativerod-shapedyes
125438negative97.476
125439negative96.9

pigmentation

  • @ref: 30338
  • production: yes

Culture and growth conditions

culture temp

@refgrowthtypetemperature
30338positivegrowth04-37
30338positiveoptimum28

culture pH

@refabilitytypepHPH range
30338positivegrowth05-09alkaliphile
30338positiveoptimum7

Physiology and metabolism

oxygen tolerance

@refoxygen toleranceconfidence
30338aerobe
125439obligate aerobe96.6

spore formation

  • @ref: 125439
  • spore formation: no
  • confidence: 97.8

halophily

  • @ref: 30338
  • salt: NaCl
  • growth: positive
  • tested relation: growth
  • concentration: 0-4 %

metabolite utilization

@refChebi-IDmetaboliteutilization activitykind of utilization tested
30338370543-hydroxybutyrate+carbon source
3033816449alanine+carbon source
3033817234glucose+carbon source
3033824996lactate+carbon source
3033825115malate+carbon source
3033826271proline+carbon source
3033826546rhamnose+carbon source
3033833942ribose+carbon source
3033817814salicin+carbon source

enzymes

@refvalueactivityec
30338catalase+1.11.1.6
30338cytochrome oxidase+1.9.3.1

Isolation, sampling and environmental information

isolation

  • @ref: 30338
  • sample type: soil

isolation source categories

  • Cat1: #Environmental
  • Cat2: #Terrestrial
  • Cat3: #Soil

taxonmaps

  • @ref: 69479
  • File name: preview.99_68240.png
  • url: https://microbeatlas.org/index.html?action=taxon&taxon_id=90_47;96_7252;97_8697;98_48050;99_68240&stattab=map
  • Last taxonomy: Skermanella stibiiresistens subclade
  • 16S sequence: HQ315828
  • Sequence Identity:
  • Total samples: 412
  • soil counts: 237
  • aquatic counts: 20
  • animal counts: 118
  • plant counts: 37

Sequence information

16S sequences

  • @ref: 30338
  • description: Skermanella stibiiresistens SB22 16S ribosomal RNA gene, partial sequence
  • accession: HQ315828
  • length: 1420
  • database: nuccore
  • NCBI tax ID: 1385369

Genome sequences

@refdescriptionaccessionassembly leveldatabaseNCBI tax ID
66792Skermanella stibiiresistens SB22GCA_000576635contigncbi1385369
66792Skermanella stibiiresistens SB221385369.3wgspatric1385369
66792Skermanella stibiiresistens SB222590828654draftimg1385369

GC content

  • @ref: 30338
  • GC-content: 69.6

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingno97.476yes
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no87.984yes
125438spore-formingspore-formingAbility to form endo- or exosporesno85.895no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)yes81.021yes
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno97.439yes
125438motile2+flagellatedAbility to perform flagellated movementyes78.884no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno97.8
125439BacteriaNetmotilityAbility to perform movementyes68.9
125439BacteriaNetgram_stainReaction to gram-stainingnegative96.9
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthobligate aerobe96.6

External links

@ref: 30338

culture collection no.: CGMCC 1.10751, KCTC 23364

literature

  • topic: Phylogeny
  • Pubmed-ID: 21784960
  • title: Skermanella stibiiresistens sp. nov., a highly antimony-resistant bacterium isolated from coal-mining soil, and emended description of the genus Skermanella.
  • authors: Luo G, Shi Z, Wang H, Wang G
  • journal: Int J Syst Evol Microbiol
  • DOI: 10.1099/ijs.0.033746-0
  • year: 2011
  • mesh: Antimony/*metabolism, Base Composition, China, Coal Mining, DNA, Bacterial/genetics, Fatty Acids/metabolism, Molecular Sequence Data, Phylogeny, RNA, Ribosomal, 16S/genetics, Rhodospirillaceae/*classification/genetics/*isolation & purification/metabolism, *Soil Microbiology
  • topic2: Metabolism

Reference

@idauthorstitledoi/urlID_cross_referencepubmed
20215Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ10.1099/ijsem.0.004332
30338Barberan A, Caceres Velazquez H, Jones S, Fierer N.Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information10.1128/mSphere.00237-172667928776041
66792Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg OvermannAutomatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information)https://diaspora-project.de/progress.html#genomes
69479João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.MicrobeAtlas 1.0 betahttps://microbeatlas.org/
125438Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg OvermannPredicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets10.1101/2024.08.12.607695
125439Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardydeepG: Deep Learning for Genome Sequence Data. R package version 0.3.1https://github.com/GenomeNet/deepG