Strain identifier

BacDive ID: 158984

Type strain: No

Species: Ligilactobacillus agilis

Strain Designation: WCA-389-WT-5H1

Strain history: <- D. Wylensek, TU Munich, Freising-Weihenstephan, Germany; WCA-389-WT-5H1

NCBI tax ID(s): 1601 (species)

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General

@ref: 65807

BacDive-ID: 158984

DSM-Number: 102821

keywords: genome sequence, 16S sequence, Bacteria, mesophilic, Gram-positive

description: Ligilactobacillus agilis WCA-389-WT-5H1 is a mesophilic, Gram-positive bacterium that was isolated from feces; pig, 3.5 month old, wild type.

NCBI tax id

  • NCBI tax id: 1601
  • Matching level: species

strain history

  • @ref: 65807
  • history: <- D. Wylensek, TU Munich, Freising-Weihenstephan, Germany; WCA-389-WT-5H1

doi: 10.13145/bacdive158984.20250331.9.3

Name and taxonomic classification

LPSN

  • @ref: 20215
  • description: domain/bacteria
  • keyword: phylum/bacillota
  • domain: Bacteria
  • phylum: Bacillota
  • class: Bacilli
  • order: Lactobacillales
  • family: Lactobacillaceae
  • genus: Ligilactobacillus
  • species: Ligilactobacillus agilis
  • full scientific name: Ligilactobacillus agilis (Weiss et al. 1982) Zheng et al. 2020
  • synonyms

    • @ref: 20215
    • synonym: Lactobacillus agilis

@ref: 65807

domain: Bacteria

phylum: Firmicutes

class: Bacilli

order: Lactobacillales

family: Lactobacillaceae

genus: Ligilactobacillus

species: Ligilactobacillus agilis

full scientific name: Ligilactobacillus agilis (Weiss et al. 1982) Zheng et al. 2020

strain designation: WCA-389-WT-5H1

type strain: no

Morphology

cell morphology

  • @ref: 125438
  • gram stain: positive
  • confidence: 90.01

Culture and growth conditions

culture medium

@refnamegrowthlinkcomposition
65807MRS MEDIUM (DSMZ Medium 11)yeshttps://mediadive.dsmz.de/medium/11Name: MRS MEDIUM (DSMZ Medium 11) Composition: Glucose 20.0 g/l Casein peptone 10.0 g/l Meat extract 10.0 g/l Na-acetate 5.0 g/l Yeast extract 5.0 g/l (NH4)3 citrate 2.0 g/l K2HPO4 2.0 g/l Tween 80 1.0 g/l MgSO4 x 7 H2O 0.2 g/l MnSO4 x H2O 0.05 g/l Distilled water
65807WILKINS-CHALGREN ANAEROBE BROTH (DSMZ Medium 339)yeshttps://mediadive.dsmz.de/medium/339Name: WILKINS-CHALGREN ANAEROBE BROTH (DSMZ Medium 339) Composition: dehydrated Wilkins-Chalgren medium 33.0 g/l L-Cysteine HCl 0.3 g/l Sodium resazurin 0.0005 g/l Distilled water

culture temp

  • @ref: 65807
  • growth: positive
  • type: growth
  • temperature: 37

Physiology and metabolism

oxygen tolerance

  • @ref: 125439
  • oxygen tolerance: anaerobe
  • confidence: 97.4

Isolation, sampling and environmental information

isolation

  • @ref: 65807
  • sample type: feces; pig, 3.5 month old, wild type
  • geographic location: Kranzberg (Bavaria)
  • country: Germany
  • origin.country: DEU
  • continent: Europe

isolation source categories

Cat1Cat2Cat3
#Host Body Product#Gastrointestinal tract#Feces (Stool)
#Host#Mammals#Suidae (Pig,Swine)

taxonmaps

  • @ref: 69479
  • File name: preview.99_6918.png
  • url: https://microbeatlas.org/index.html?action=taxon&taxon_id=90_45;96_3250;97_3988;98_5089;99_6918&stattab=map
  • Last taxonomy: Ligilactobacillus agilis subclade
  • 16S sequence: MN537457
  • Sequence Identity:
  • Total samples: 41290
  • soil counts: 1642
  • aquatic counts: 1961
  • animal counts: 36795
  • plant counts: 892

Safety information

risk assessment

  • @ref: 65807
  • biosafety level: 1
  • biosafety level comment: Risk group (German classification)

Sequence information

16S sequences

  • @ref: 65807
  • description: Ligilactobacillus agilis strain DSM 102821 16S ribosomal RNA gene, partial sequence
  • accession: MN537457
  • length: 1511
  • database: nuccore
  • NCBI tax ID: 1601

Genome sequences

  • @ref: 66792
  • description: Ligilactobacillus agilis WCA-389-WT-5H1
  • accession: GCA_012843665
  • assembly level: contig
  • database: ncbi
  • NCBI tax ID: 1601

Genome-based predictions

predictions

@refmodeltraitdescriptionpredictionconfidencetraining_data
125438gram-positivegram-positivePositive reaction to Gram-stainingyes90.01no
125438anaerobicanaerobicAbility to grow under anoxygenic conditions (including facultative anaerobes)no82.477no
125438aerobicaerobicAbility to grow under oxygenic conditions (including facultative aerobes)no98no
125438spore-formingspore-formingAbility to form endo- or exosporesno79.38no
125438thermophilethermophilicAbility to grow at temperatures above or equal to 45°Cno94.333yes
125438motile2+flagellatedAbility to perform flagellated movementyes59.833no
125439BacteriaNetspore_formationAbility to form endo- or exosporesno58.5
125439BacteriaNetmotilityAbility to perform movementno58.4
125439BacteriaNetgram_stainReaction to gram-stainingvariable68.2
125439BacteriaNetoxygen_toleranceOxygenic conditions needed for growthanaerobe97.4

External links

@ref: 65807

culture collection no.: DSM 102821

straininfo link

  • @ref: 111500
  • straininfo: 400520

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
65807Curators of the DSMZhttps://www.dsmz.de/collection/catalogue/details/culture/DSM-102821Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSM 102821)
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/
111500Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.StrainInfo: A central database for resolving microbial strain identifiers10.60712/SI-ID400520.1
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