Chryseobacterium taihuense (CGMCC 1.10941, NBRC 108747, THMBM1)

Name: Chryseobacterium taihuense (CGMCC 1.10941, NBRC 108747, THMBM1)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 133828

Type strain

Strain Designation THMBM1

Culture col. no. CGMCC 1.10941 NBRC 108747 THMBM1

NCBI tax ID(s) 1141221

Special Collections Literature strains

Links

Chryseobacterium taihuense THMBM1 is a facultative anaerobe, Gram-negative, rod-shaped bacterium that was isolated from decomposing algal scum.

Gram-negative rod-shaped facultative anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 390821

NBRC 108747

@ref 20215

Last LPSN update 2026-02-09 11:32:02

Domain Bacteria

Phylum Bacteroidota

Class Flavobacteriia

Order Flavobacteriales

Family Weeksellaceae

Genus Chryseobacterium

Species Chryseobacterium taihuense

Full scientific name Chryseobacterium taihuense Wu et al. 2013

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
30620	negative	1.5 µm	0.5 µm	rod-shaped
125439	negative				99.9
125438	negative				96.455

Pigmentation

30620

Productionyes

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
30620	positive	growth	15-45
30620	positive	optimum	33.5

Culture pH

@ref	Ability	Type	PH	PH range
30620	positive	optimum	8
30620	positive	growth	05-09	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
30620	facultative anaerobe
125439	obligate aerobe	95

Spore formation

@ref	Spore formation	Confidence
30620
125439		99.4
125438		90.76

Halophily

@ref	Salt	Growth	Tested relation	Concentration
30620	NaCl	positive	growth	0-4 %
30620	NaCl	positive	optimum	0.5 %

Observation

30620

Observationaggregates in clumps

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30620	27689 ChEBI	decanoate	+	carbon source
30620	4853 ChEBI	esculin	+	hydrolysis
30620	24265 ChEBI	gluconate	+	carbon source
30620	17234 ChEBI	glucose	+	carbon source
30620	25115 ChEBI	malate	+	carbon source
30620	17306 ChEBI	maltose	+	carbon source
30620	29864 ChEBI	mannitol	+	carbon source
30620	37684 ChEBI	mannose	+	carbon source

Metabolite production

@ref	Chebi-ID	Metabolite	Production
30620	35581 ChEBI	indole

Metabolite tests

@ref	Chebi-ID	Metabolite	Indole test
30620	35581 ChEBI	indole	+

Enzymes

@ref	Value	Activity	Ec
30620	acid phosphatase	+	3.1.3.2
30620	alkaline phosphatase	+	3.1.3.1
30620	catalase	+	1.11.1.6
30620	cytochrome oxidase	+	1.9.3.1
30620	gelatinase	+
30620	urease	+	3.5.1.5

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Host	#Algae

Isolation

30620

Sample typedecomposing algal scum

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	IMG-taxon 2663762742 annotated assembly for Chryseobacterium taihuense CGMCC 1.10941	scaffold	GCA_900103755	1141221.4	2663762742	1141221	71.87

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
30620	Chryseobacterium taihuense strain THMBM1 16S ribosomal RNA gene, partial sequence	JQ283114	1478		1141221

GC content

30620

GC-content (mol%)36.8

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Chryseobacterium taihuense CGMCC 1.10941
NCBI: GCA_900103755

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.40	no
125439	motility	BacteriaNetⓘ	no	78.30	no
125439	gram_stain	BacteriaNetⓘ	negative	99.90	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	95.00	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Chryseobacterium taihuense CGMCC 1.10941
NCBI: GCA_900103755.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.46	yes
125438	anaerobic	anaerobicⓘ	no	97.80	no
125438	aerobic	aerobicⓘ	yes	83.14	yes
125438	spore-forming	spore-formingⓘ	no	90.76	yes
125438	thermophilic	thermophileⓘ	no	97.14	yes
125438	flagellated	motile2+ⓘ	no	89.50	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
	Phylogenetic insights into the diversity of Chryseobacterium species.	Siddaramappa S, Narjala A, Viswanathan V, Maliye C, Lakshminarayanan R.	Access Microbiol	10.1099/acmi.0.000019	2019
Phylogeny	Chryseobacterium lineare sp. nov., isolated from a limpid stream.	Zhao Z, Tu YQ, Shen X, Han SB, Zhang CY, Sun C, Wu M	Int J Syst Evol Microbiol	10.1099/ijsem.0.001629	2017
Phylogeny	Chryseobacterium taihuense sp. nov., isolated from a eutrophic lake, and emended descriptions of the genus Chryseobacterium, Chryseobacterium taiwanense, Chryseobacterium jejuense and Chryseobacterium indoltheticum.	Wu YF, Wu QL, Liu SJ	Int J Syst Evol Microbiol	10.1099/ijs.0.040337-0	2012
Phylogeny	Chryseobacterium echinoideorum sp. nov., isolated from sea urchins (Tripneustes gratilla).	Lin SY, Hameed A, Wen CZ, Liu YC, Shen FT, Hsu YH, Lai WA, Young CC	Int J Syst Evol Microbiol	10.1099/ijsem.0.000524	2015
Phylogeny	Chryseobacterium oryzae sp. nov. and Chryseobacterium suipulveris sp. nov., isolated from Andong sikhye and pigpen dust, respectively.	Heo J, Won M, Lee D, Kim JS, Han BH, Kim SJ, Hong SB, Kwon SW	Int J Syst Evol Microbiol	10.1099/ijsem.0.005532	2022

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 )
#26951	IJSEM 913 2013 ( DOI 10.1099/ijs.0.040337-0 , PubMed 22634705 )
#30620	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 #26951
#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) .
#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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Chryseobacterium taihuense (CGMCC 1.10941, NBRC 108747, THMBM1)

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

Metabolite production

Metabolite tests

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_900103755

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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