Methylobacterium gnaphalii (DSM 24027, NBRC 107716, 23e)

Name: Methylobacterium gnaphalii (DSM 24027, NBRC 107716, 23e)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7200

Type strain

Strain Designation 23e

Culture col. no. DSM 24027 NBRC 107716 23e

NCBI tax ID(s) 1010610

Special Collections DSMZ Literature strains

Links

Methylobacterium gnaphalii 23e is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from leaves of Gnaphalium spicatum .

Gram-negative motile rod-shaped aerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 397058

NBRC 107716,DSM 24027

@ref 20215

Last LPSN update 2025-12-16 09:48:31

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Methylobacteriaceae

Genus Methylobacterium

Species Methylobacterium gnaphalii

Full scientific name Methylobacterium gnaphalii Tani et al. 2012

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape
30510	negative	1.99 µm	0.9 µm	rod-shaped
125438	negative
125439	negative

Colony morphology

17479

Incubation period3-7 days

Pigmentation

30510

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
17479	R2A MEDIUM (DSMZ Medium 830)		Medium recipe at MediaDive	Name: R2A MEDIUM (DSMZ Medium 830) Composition: Agar 15.0 g/l Casamino acids 0.5 g/l Starch 0.5 g/l Glucose 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
17479	positive	growth	28	mesophilic
30510	positive	growth	28-37	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance
30510	aerobe
125439	obligate aerobe

Spore formation

@ref	Spore formation	Confidence
125439		97.1

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30510	30089 ChEBI	acetate	+	carbon source
30510	22599 ChEBI	arabinose	+	carbon source
30510	15740 ChEBI	formate	+	carbon source
30510	29987 ChEBI	glutamate	+	carbon source
30510	24996 ChEBI	lactate	+	carbon source
30510	15792 ChEBI	malonate	+	carbon source
30510	51850 ChEBI	methyl pyruvate	+	carbon source
30510	17632 ChEBI	nitrate	+	reduction
30510	17272 ChEBI	propionate	+	carbon source
30510	30031 ChEBI	succinate	+	carbon source

Enzymes

@ref	Value	Activity	Ec
30510	catalase	+	1.11.1.6
30510	urease	+	3.5.1.5

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Plants	#Herbaceous plants (Grass,Crops)
#Host Body-Site	#Plant	#Leaf (Phyllosphere)

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
17479	leaves of Gnaphalium spicatum (cudweed)	Gnaphalium spicatum	Okayama, Okayama University at the Institute of Plant Science and Resources	Japan	JPN	Asia

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
17479	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM799221v1 assembly for Methylobacterium gnaphalii NBRC 107716	contig	GCA_007992215	1010610.3	2996149227	1010610	55.12
124043	ASM3016093v1 assembly for Methylobacterium gnaphalii NBRC 107716	scaffold	GCA_030160935	1010610.5	8119303486	1010610	47.63
66792	ASM2217920v1 assembly for Methylobacterium gnaphalii DSM 24027	contig	GCA_022179205	1010610.4	8111886852	1010610	39.18

Genome browser: GCA_007992215

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
17479	Methylobacterium gnaphalii gene for 16S ribosomal RNA, partial sequence	AB627071	1445		1010610

GC content

@ref	GC-content (mol%)	Method
17479	67.2	thermal denaturation, midpoint method (Tm)
30510	67.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methylobacterium gnaphalii NBRC 107716
NCBI: GCA_007992215

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	97.70	no
125439	gram_stain	BacteriaNetⓘ	negative	95.90	no
125439	motility	BacteriaNetⓘ	yes	54.10	no
125439	spore_formation	BacteriaNetⓘ	no	97.10	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methylobacterium gnaphalii strain NBRC 107716
PATRIC: 1010610.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.83	no
125438	anaerobic	anaerobicⓘ	no	94.06	no
125438	spore-forming	spore-formingⓘ	no	86.06	no
125438	aerobic	aerobicⓘ	yes	87.22	yes
125438	thermophilic	thermophileⓘ	no	98.40	no
125438	flagellated	motile2+ⓘ	yes	70.94	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Methylobacterium gnaphalii sp. nov., isolated from leaves of Gnaphalium spicatum.	Tani A, Sahin N, Kimbara K	Int J Syst Evol Microbiol	10.1099/ijs.0.037713-0	2011

References

#17479	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 24027
#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 )
#30510	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 #26842 (see below)
#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) .
#124043	Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy .
#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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Methylobacterium gnaphalii (DSM 24027, NBRC 107716, 23e)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_007992215

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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