Strain identifier

BacDive ID: 169742

Type strain: Yes

Species: Adlercreutzia aquisgranensis

Strain Designation: Cla-RA-2, CLA-SR-PSM1-47, RA2

Strain history: <- T. Clavel, RWTH Aachen; CLA-SR-PSM1-47

NCBI tax ID(s): 2941323 (species)

For citation purpose refer to the digital object identifier (doi) of the current version.
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General

@ref: 69161

BacDive-ID: 169742

DSM-Number: 108611

keywords: genome sequence, Bacteria, mesophilic

description: Adlercreutzia aquisgranensis Cla-RA-2 is a mesophilic bacterium that was isolated from gut content; wild mouse.

NCBI tax id

  • NCBI tax id: 2941323
  • Matching level: species

strain history

  • @ref: 69161
  • history: <- T. Clavel, RWTH Aachen; CLA-SR-PSM1-47

doi: 10.13145/bacdive169742.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/actinomycetota
  • domain: Bacteria
  • phylum: Actinomycetota
  • class: Coriobacteriia
  • order: Eggerthellales
  • family: Eggerthellaceae
  • genus: Adlercreutzia
  • species: Adlercreutzia aquisgranensis
  • full scientific name: Adlercreutzia aquisgranensis Afrizal et al. 2024

@ref: 69161

domain: Bacteria

phylum: Actinobacteria

class: Coriobacteriia

order: Eggerthellales

family: Eggerthellaceae

genus: Adlercreutzia

species: Adlercreutzia aquisgranensis

full scientific name: Adlercreutzia aquisgranensis

strain designation: Cla-RA-2, CLA-SR-PSM1-47, RA2

type strain: yes

Morphology

cell morphology

  • @ref: 125439
  • gram stain: negative
  • confidence: 93.8

Culture and growth conditions

culture medium

  • @ref: 69161
  • name: WILKINS-CHALGREN ANAEROBE BROTH (DSMZ Medium 339)
  • growth: yes
  • link: https://www.dsmz.de/microorganisms/medium/pdf/DSMZ_Medium339.pdf

culture temp

  • @ref: 69161
  • growth: positive
  • type: growth
  • temperature: 37

Physiology and metabolism

spore formation

  • @ref: 125439
  • spore formation: no
  • confidence: 98.7

Isolation, sampling and environmental information

isolation

  • @ref: 69161
  • sample type: gut content; wild mouse
  • geographic location: Aachen
  • country: Germany
  • origin.country: DEU
  • continent: Europe

Sequence information

Genome sequences

  • @ref: 66792
  • description: Adlercreutzia sp. DSM 108611 Adlercreutzia_sp_nov3_DSM108611
  • accession: GCA_943193145
  • assembly level: contig
  • database: ncbi
  • NCBI tax ID: 2941323

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingyes81.852no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)yes86.894no
125438spore-formingspore-formingAbility to form endo- or exosporesno77.377no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no93.522no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno91.319yes
125438motile2+flagellatedAbility to perform flagellated movementno86.5no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno98.7
125439BacteriaNetmotilityAbility to perform movementyes85.7
125439BacteriaNetgram_stainReaction to gram-stainingnegative93.8
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthanaerobe82.3

External links

@ref: 69161

culture collection no.: DSM 108611

Reference

@idauthorstitledoi/urlcatalogue
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
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
69161Curators of the DSMZhttps://www.dsmz.de/collection/catalogue/details/culture/DSM-108611Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSM 108611)
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