Methylobacterium iners (DSM 19015, KACC 11765, 5317S-33)

Name: Methylobacterium iners (DSM 19015, KACC 11765, 5317S-33)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7183

Type strain

Strain Designation 5317S-33

Culture col. no. DSM 19015 KACC 11765 5317S-33 JCM 16407

NCBI tax ID(s) 418707

Special Collections DSMZ JCM Literature strains

History <- S.-W. Kwon, KACC; 5317S-33 KACC 11765 <-- H.-Y. Weon 5317S-33.

Links

Methylobacterium iners 5317S-33 is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from air sample.

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

Name and taxonomic classification

SI-ID 323974

DSM 19015,KACC 11765,JCM 16407,LMG 28248,NCIMB 14630

@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 iners

Full scientific name Methylobacterium iners Weon et al. 2008

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
32155	negative	2 µm	1 µm	rod-shaped
125439	negative				95.2
125438	negative				96.815

Pigmentation

32155

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
7832	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
7832	positive	growth	30	mesophilic
32155	positive	growth	10-30
32155	positive	optimum	26.5	mesophilic
67770	positive	growth	30	mesophilic

Culture pH

@ref	Ability	Type	PH
32155	positive	optimum	7
32155	positive	growth	06-08

Physiology and metabolism

Oxygen tolerance

32155

Oxygen toleranceaerobe

Spore formation

@ref	Spore formation	Confidence
125439		97.7

Halophily

@ref	Salt	Growth	Tested relation	Concentration
32155	NaCl	positive	growth	<2 %

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
32155	30089 ChEBI	acetate	+	carbon source
32155	22653 ChEBI	asparagine	+	carbon source
32155	35391 ChEBI	aspartate	+	carbon source
32155	15740 ChEBI	formate	+	carbon source
32155	29987 ChEBI	glutamate	+	carbon source
32155	24996 ChEBI	lactate	+	carbon source
32155	51850 ChEBI	methyl pyruvate	+	carbon source
32155	17272 ChEBI	propionate	+	carbon source
32155	30031 ChEBI	succinate	+	carbon source

Enzymes

@ref	Value	Activity	Ec
32155	catalase	+	1.11.1.6
32155	cytochrome oxidase	+	1.9.3.1

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Environmental	#Air

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
7832	air sample	Suwon city	Republic of Korea	KOR	Asia
67770	Air sample in Suwon City		Republic of Korea	KOR	Asia

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
7832	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
124043	ASM4265712v1 assembly for Methylobacterium iners KACC 11765	contig	GCA_042657125			418707	49.4
66792	ASM2217930v1 assembly for Methylobacterium iners DSM 19015	contig	GCA_022179305	418707.3	8069261393	418707	44.31

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
7832	Methylobacterium iners strain 5317S-33 16S ribosomal RNA gene, partial sequence	EF174497	1353		418707

GC content

@ref	GC-content (mol%)	Method
7832	68.0
32155	68
67770	68	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: Methylobacterium iners DSM 19015
NCBI: GCA_022179305

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	97.70	no
125439	motility	BacteriaNetⓘ	yes	67.80	no
125439	gram_stain	BacteriaNetⓘ	negative	95.20	no
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	83.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methylobacterium iners DSM 19015
NCBI: GCA_022179305.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.82	yes
125438	anaerobic	anaerobicⓘ	no	91.79	no
125438	aerobic	aerobicⓘ	yes	81.51	no
125438	spore-forming	spore-formingⓘ	no	84.46	no
125438	thermophilic	thermophileⓘ	no	96.83	yes
125438	flagellated	motile2+ⓘ	yes	74.09	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Methylobacterium oxalidis sp. nov., isolated from leaves of Oxalis corniculata.	Tani A, Sahin N, Kimbara K	Int J Syst Evol Microbiol	10.1099/ijs.0.033019-0	2011
Phylogeny	Methylobacterium gossipiicola sp. nov., a pink-pigmented, facultatively methylotrophic bacterium isolated from the cotton phyllosphere.	Madhaiyan M, Poonguzhali S, Senthilkumar M, Lee JS, Lee KC	Int J Syst Evol Microbiol	10.1099/ijs.0.030148-0	2011
Phylogeny	Methylobacterium iners sp. nov. and Methylobacterium aerolatum sp. nov., isolated from air samples in Korea.	Weon HY, Kim BY, Joa JH, Son JA, Song MH, Kwon SW, Go SJ, Yoon SH	Int J Syst Evol Microbiol	10.1099/ijs.0.65047-0	2008

References

#7832	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 19015
#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 )
#28398	IJSEM 93 2008 ( DOI 10.1099/ijs.0.65047-0 , PubMed 18175690 )
#32155	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 #28398
#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;
#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 )

Rate our new design

Content

About us

Methylobacterium iners (DSM 19015, KACC 11765, 5317S-33)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_042657125

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

Help

Social Media