Yeosuana aromativorans (KCCM 42019, JCM 12862, GW1-1)

Name: Yeosuana aromativorans (KCCM 42019, JCM 12862, GW1-1)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 134176

Type strain

Strain Designation GW1-1

Culture col. no. KCCM 42019 JCM 12862 GW1-1 NCIMB 14311 CIP 109255

NCBI tax ID(s) 288019

Special Collections JCM CIP Literature strains

History JCM <-- K. K. Kwon and S.-J. Kim GW1-1.

Links

Yeosuana aromativorans GW1-1 is an aerobe, Gram-negative, rod-shaped bacterium that was isolated from estuarine sediment.

Gram-negative rod-shaped aerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 291018

JCM 12862,CIP 109255

@ref 20215

Last LPSN update 2026-05-29 08:38:16

Domain Bacteria

Phylum Bacteroidota

Class Flavobacteriia

Order Flavobacteriales

Family Flavobacteriaceae

Genus Yeosuana

Species Yeosuana aromativorans

Full scientific name Yeosuana aromativorans Kwon et al. 2006

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
31697	negative	0.7-2 µm	0.2-0.3 µm	rod-shaped
125439	negative				99.162
125438					94
125438	negative				98

Pigmentation

31697

Productionyes

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
31697	positive	growth	23-39
31697	positive	optimum	33-36
67770	positive	growth	30

Culture pH

@ref	Ability	Type	PH
31697	positive	optimum	7
31697	positive	growth	05-08

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
31697	aerobe
125439	obligate aerobe	97.114

Spore formation

@ref	Spore formation	Confidence
31697
125438		91.975
125439		99.155

Halophily

@ref	Salt	Growth	Tested relation	Concentration
31697	NaCl	positive	growth	0.5-3 %
31697	NaCl	positive	optimum	1 %

Observation

@ref	Observation
31697	aggregates in chains
67770	quinones: MK-5, MK-6

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
31697	18240 ChEBI	4-hydroxy-L-proline	+	carbon source
31697	16449 ChEBI	alanine	+	carbon source
31697	22653 ChEBI	asparagine	+	carbon source
31697	35391 ChEBI	aspartate	+	carbon source
31697	28260 ChEBI	galactose	+	carbon source
31697	29987 ChEBI	glutamate	+	carbon source
31697	26986 ChEBI	threonine	+	carbon source

Enzymes

@ref	Value	Activity	Ec
31697	catalase	+	1.11.1.6

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Estuary
#Environmental	#Aquatic	#Sediment
#Environmental	#Aquatic	#Brackish

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
31697	estuarine sediment
67770	Estuarine sediment of Gwangyang Bay	Yeosu City	Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence AY682382 (>99% sequence identity) for Yeosuana aromativorans subclade from Microbeatlas

Aquatic Samples	127
Soil Samples	2
Animal Samples	3
Plant Samples	10
Total Samples	142

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1464665v1 assembly for Yeosuana aromativorans JCM 12862	scaffold	GCA_014646655	288019.3	2989133471	288019	69.52

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
31697	Yeosuana aromativorans 16S ribosomal RNA gene, partial sequence	AY682382	1469		288019

GC content

@ref	GC-content (mol%)	Method
31697	51.4
67770	51.4	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM1464665v1 assembly for Yeosuana aromativorans JCM 12862
BV-BRC: 288019.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	97.11	no
125439	motility	BacteriaNetⓘ	no	80.78	no
125439	gram_stain	BacteriaNetⓘ	negative	99.16	no
125439	spore_formation	BacteriaNetⓘ	no	99.16	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Yeosuana aromativorans JCM 12862
NCBI: GCA_014646655.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.00	no
125438	anaerobic	anaerobicⓘ	no	95.86	yes
125438	aerobic	aerobicⓘ	yes	78.92	yes
125438	spore-forming	spore-formingⓘ	no	91.98	yes
125438	thermophilic	thermophileⓘ	no	96.95	yes
125438	flagellated	motile2+ⓘ	no	94.00	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Polyphasic and comparative genomic characterization of a novel Mariniflexile species in the rhizosphere microbiome of tomato resistant to bacterial wilt.	Kwak MJ, Park J, Park H, Yoon J, Lee J, Hahnke RL, Lee SW, Kwon SK, Song JY, Kim JF.	Sci Rep	10.1038/s41598-025-18301-6	2025
Phylogeny	Mariniflexile maritimum sp. nov., isolated from seawater of the South Sea in the Republic of Korea.	Ko SR, Le VV, Jin L, Lee SA, Ahn CY, Oh HM	Int J Syst Evol Microbiol	10.1099/ijsem.0.004925	2021
Phylogeny	Yeosuana aromativorans gen. nov., sp. nov., a mesophilic marine bacterium belonging to the family Flavobacteriaceae, isolated from estuarine sediment of the South Sea, Korea.	Kwon KK, Lee HS, Jung HB, Kang JH, Kim SJ	Int J Syst Evol Microbiol	10.1099/ijs.0.64073-0	2006
Phylogeny	Snuella sedimenti sp. nov., isolated from marine sediment.	Kim JH, Weerawongwiwat V, Yoon JH, Lee JS, Sukhoom A, Kim W	Arch Microbiol	10.1007/s00203-021-02528-8	2021
Phylogeny	Yeosuana marina sp. nov., isolated from shallow-sea hydrothermal systems off Kueishantao Island.	Zhang Q, Lin D, Ye J, Lin TH, Li C, Lin H, Sun X, Tang K	Int J Syst Evol Microbiol	10.1099/ijsem.0.004525	2020

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 )
#27974	IJSEM 727 2006 ( DOI 10.1099/ijs.0.64073-0 , PubMed 16585684 )
#31697	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #27974
#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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Yeosuana aromativorans (KCCM 42019, JCM 12862, GW1-1)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Observation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_014646655

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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