Weissella thailandensis (DSM 103409, JCM 30580, KCCM 43128)

Name: Weissella thailandensis (DSM 103409, JCM 30580, KCCM 43128)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 132289

Type strain

Strain Designation FOL01

Culture col. no. DSM 103409 JCM 30580 KCCM 43128 FOL01 LMG 30916 1 more

NCBI tax ID(s) 1631871

Special Collections DSMZ JCM

History DSMZ <- JCM Riken BioResource Center, Tsukuba, Japan; JCM 30580 <- J.-H. Lee; Kyung Hee Univ., South Korea; FOL01 JCM <-- J.-H. Lee; Kyung Hee Univ., South Korea; FOL01.

Links

Weissella thailandensis FOL01 is a bacterium that was isolated from jogae jeotgal, traditional Korean fermented seafood.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 398060

LMG 30916,JCM 30580,DSM 103409

@ref 20215

Last LPSN update 2026-05-29 08:37:49

Domain Bacteria

Phylum Bacillota

Class Bacilli

Order Lactobacillales

Family Lactobacillaceae

Genus Weissella

Species Weissella thailandensis

Full scientific name Weissella thailandensis Tanasupawat et al. 2000

Synonyms (1)

Weissella jogaejeotgali

BacDive ID	Other strains from **Weissella thailandensis** (2)	Type strain
6855	W. thailandensis FS61-1, DSM 15832, HSCC 1412, JCM 10695, ... (type strain)
153767	W. thailandensis CCUG 50906

Morphology

Cell morphology

@ref	Motility	Confidence	Gram stain
125438		92
125438		95.106	positive

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
24078	MRS MEDIUM (DSMZ Medium 11)		Medium recipe at MediaDive	Name: 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

Culture temp

@ref	Growth	Type	Temperature (°C)
24078	positive	growth	37
67770	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	facultative anaerobe	96.145

Spore formation

@ref	Spore formation	Confidence
125439		94.033

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Food production	#Seafood
#Engineered	#Food production	#Fermented

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
24078	jogae jeotgal, traditional Korean fermented seafood	Incheon	Republic of Korea	KOR	Asia
67770	Jogae jeotgal, a Korean traditional fermented seafood	Incheon	Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence KP027016 (>99% sequence identity) for Weissella from Microbeatlas

Aquatic Samples	1125
Soil Samples	1853
Animal Samples	11172
Plant Samples	765
Total Samples	14915

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
24078	1	Risk group (German classification)

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	IMG accession	NCBI tax ID	Score
66792	ASM193261v1 assembly for Weissella jogaejeotgali FOL01	complete	GCA_001932615	8114062129	1631871	97.94

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
24078	Weissella jogaejeotgali strain FOL01 16S ribosomal RNA (rrnA) gene, partial sequence	KP027016	1336		1631871

GC content

@ref	GC-content (mol%)	Method
24078	39.6	fluorimetric
67770	39.6	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM193261v1 assembly for Weissella jogaejeotgali FOL01
NCBI: GCA_001932615

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	96.15	no
125439	motility	BacteriaNetⓘ	no	87.19	no
125439	gram_stain	BacteriaNetⓘ	positive	86.14	no
125439	spore_formation	BacteriaNetⓘ	no	94.03	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Weissella jogaejeotgali strain FOL01
PATRIC: 1631871.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	95.11	no
125438	anaerobic	anaerobicⓘ	no	88.03	no
125438	spore-forming	spore-formingⓘ	no	87.72	no
125438	aerobic	aerobicⓘ	no	91.77	no
125438	thermophilic	thermophileⓘ	no	96.50	yes
125438	flagellated	motile2+ⓘ	no	92.00	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Biotechnology	Genomic Insights of Weissella jogaejeotgali FOL01(T) Reveals Its Food Fermentation Ability and Human Gut Adaptive Potential for Probiotic Applications in Food Industries.	Ku HJ, Kim YT, Lee JH	J Microbiol Biotechnol	10.4014/jmb.1702.02054	2017
Phylogeny	Genome-based reclassification of Weissella jogaejeotgali as a later heterotypic synonym of Weissella thailandensis.	Kwak MJ, Choi SB, Kim BY, Chun J	Int J Syst Evol Microbiol	10.1099/ijsem.0.003315	2019
Phylogeny	Weissella jogaejeotgali sp. nov., isolated from jogae jeotgal, a traditional Korean fermented seafood.	Lee SH, Ku HJ, Ahn MJ, Hong JS, Lee SH, Shin H, Lee KC, Lee JS, Ryu S, Jeon CO, Lee JH	Int J Syst Evol Microbiol	10.1099/ijsem.0.000631	2015

References

#20215	Parte, 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 DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 )
#24078	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 103409
#66792	Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann: Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#69479	João 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 beta .
#125438	Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 )
#125439	Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 .
#126262	A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 )

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Weissella thailandensis (DSM 103409, JCM 30580, KCCM 43128)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_001932615

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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