Meiothermus cateniformans (CGMCC 1.6990, JCM 15151, LY 1)

Name: Meiothermus cateniformans (CGMCC 1.6990, JCM 15151, LY 1)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 133487

Type strain

Culture col. no. CGMCC 1.6990 JCM 15151 LY 1

NCBI tax ID(s) 172827

Special Collections JCM Literature strains

History JCM <-- X.-Q. Zhang LY1.

Links

Meiothermus cateniformans CGMCC 1.6990 is an aerobe, Gram-negative, rod-shaped bacterium that was isolated from hot spring.

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

Name and taxonomic classification

SI-ID 26658

JCM 15151

@ref 20215

Last LPSN update 2026-05-29 08:38:06

Domain Bacteria

Phylum Deinococcota

Class Deinococci

Order Thermales

Family Thermaceae

Genus Meiothermus

Species Meiothermus cateniformans

Full scientific name Meiothermus cateniformans Zhang et al. 2010

BacDive ID	Other strains from **Meiothermus cateniformans** (1)	Type strain
161582	M. cateniformans JCM 15150, CGMCC 1.6989

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility
29173	negative	1.75 µm	0.5 µm	rod-shaped

Pigmentation

29173

Productionyes

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
29173	positive	growth	55-67
29173	positive	optimum	57.5
67770	positive	growth	55

Culture pH

@ref	Ability	Type	PH	PH range
29173	positive	optimum	8.25
29173	positive	growth	5.5-10	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
29173	aerobe
125439	facultative anaerobe	93.6

Spore formation

@ref	Spore formation	Confidence
29173
125439		92.34

Observation

@ref	Observation
29173	aggregates in chains
67770	quinones: MK-7

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29173	29016 ChEBI	arginine	+	carbon source
29173	22653 ChEBI	asparagine	+	carbon source
29173	17057 ChEBI	cellobiose	+	carbon source
29173	4853 ChEBI	esculin	+	hydrolysis
29173	28757 ChEBI	fructose	+	carbon source
29173	28260 ChEBI	galactose	+	carbon source
29173	17234 ChEBI	glucose	+	carbon source
29173	29987 ChEBI	glutamate	+	carbon source
29173	17306 ChEBI	maltose	+	carbon source
29173	29864 ChEBI	mannitol	+	carbon source
29173	37684 ChEBI	mannose	+	carbon source
29173	26271 ChEBI	proline	+	carbon source
29173	15361 ChEBI	pyruvate	+	carbon source
29173	17822 ChEBI	serine	+	carbon source
29173	30911 ChEBI	sorbitol	+	carbon source
29173	18222 ChEBI	xylose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
29173	catalase	+	1.11.1.6
29173	cytochrome oxidase	+	1.9.3.1
29173	gelatinase	+

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Thermal spring
#Condition	#Thermophilic (>45°C)	-

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
29173	hot spring
67770	Hot spring in Qianshan	Anshan	China	CHN	Asia

Taxonmaps

69479

Global distribution of 16S sequence EU247891 (>99% sequence identity) for Meiothermus from Microbeatlas

Aquatic Samples	132
Soil Samples	4
Animal Samples	70
Plant Samples	6
Total Samples	212

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM357404v1 assembly for Meiothermus taiwanensis JCM 15151	contig	GCA_003574045	482825.3	8075058925	172827	41.72

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
29173	Meiothermus cateniformans strain LY1 16S ribosomal RNA (rrnA) gene, partial sequence	EU247891	1455		482825

GC content

@ref	GC-content (mol%)	Method
29173	61.5
67770	61.5	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: ASM357404v1 assembly for Meiothermus taiwanensis JCM 15151
NCBI: GCA_003574045

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	93.60	no
125439	gram_stain	BacteriaNetⓘ	negative	59.86	no
125439	motility	BacteriaNetⓘ	no	66.80	no
125439	spore_formation	BacteriaNetⓘ	no	92.34	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Meiothermus cateniformans strain JCM 15151
PATRIC: 482825.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	74.04	no
125438	anaerobic	anaerobicⓘ	no	89.93	yes
125438	spore-forming	spore-formingⓘ	no	72.56	no
125438	aerobic	aerobicⓘ	yes	83.81	yes
125438	thermophilic	thermophileⓘ	yes	65.67	yes
125438	flagellated	motile2+ⓘ	no	74.88	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Meiothermus luteus sp. nov., a slightly thermophilic bacterium isolated from a hot spring.	Habib N, Khan IU, Hussain F, Zhou EM, Xiao M, Dong L, Zhi XY, Li WJ	Int J Syst Evol Microbiol	10.1099/ijsem.0.002040	2017
Phylogeny	Meiothermus cateniformans sp. nov., a slightly thermophilic species from north-eastern China.	Zhang XQ, Zhang WJ, Wei BP, Xu XW, Zhu XF, Wu M	Int J Syst Evol Microbiol	10.1099/ijs.0.007914-0	2009

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 )
#25597	IJSEM 840 2010 ( DOI 10.1099/ijs.0.007914-0 , PubMed 19661497 )
#29173	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 #25597
#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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Meiothermus cateniformans (CGMCC 1.6990, JCM 15151, LY 1)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_003574045

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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