Methylocystis rosea (DSM 17261, ATCC BAA-1196, SV97)

Name: Methylocystis rosea (DSM 17261, ATCC BAA-1196, SV97)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7217

Type strain

Strain Designation SV97

Culture col. no. DSM 17261 ATCC BAA-1196 SV97

NCBI tax ID(s) 1132444 173366

Special Collections DSMZ Literature strains

History <- M. M. Svenning; SV97 <- A. G. Hestnes

Links

Methylocystis rosea SV97 is a mesophilic, Gram-negative, rod-shaped prokaryote that was isolated from a high Artic Wetland soil.

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

Name and taxonomic classification

SI-ID 290993

ATCC BAA-1196,DSM 17261,LMG 27835

@ref 20215

Last LPSN update 2026-02-09 11:19:25

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Methylocystaceae

Genus Methylocystis

Species Methylocystis rosea

Full scientific name Methylocystis rosea Wartiainen et al. 2006

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
31620	negative	1.1-2.5 µm	0.8-1.1 µm	rod-shaped
125438	negative				99
125439	negative				99.2

Pigmentation

31620

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
6861	METHYLOSARCINA QUISQUILLARUM AND M. FIBRATA MEDIUM (DSMZ Medium 921)		Medium recipe at MediaDive	Name: METHYLOSARCINA QUISQUILLARUM AND M. FIBRATA MEDIUM (DSMZ Medium 921) Composition: Agar 15.0 g/l MgSO4 x 6 H2O 1.0 g/l KNO3 1.0 g/l Na2HPO4 x 12 H2O 0.716 g/l KH2PO4 0.26 g/l CaCl2 x 2 H2O 0.2 g/l CuSO4 x 5 H2O 0.001 g/l FeSO4 x 7 H2O 0.0005 g/l ZnSO4 x 7 H2O 0.0004 g/l Fe(III)-EDTA 0.00038 g/l Na2MoO4 x 2 H2O 0.00026 g/l Na2-EDTA 0.00025 g/l CoCl2 x 6 H2O 5e-05 g/l MnCl2 x 4 H2O 2e-05 g/l H3BO3 1.5e-05 g/l NiCl2 x 6 H2O 1e-05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
6861	positive	growth	28	mesophilic
31620	positive	growth	27-37	mesophilic
31620	positive	optimum	27	mesophilic

Culture pH

@ref	Ability	Type	PH	PH range
31620	positive	optimum	5.5-9
31620	positive	growth	5.5-9	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.4

Spore formation

@ref	Spore formation	Confidence
31620
125439		93.3

Observation

31620

Observationaggregates in chains

Enzymes

@ref	Value	Activity	Ec
31620	catalase	+	1.11.1.6

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil
#Environmental	#Terrestrial	#Wetland (Swamp)
#Condition	#Psychrophilic (<10°C)	-
#Climate	#Cold	#Polar

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
6861	a high Artic Wetland soil	Ny-Alesund, Svalbard Islands	Norway	NOR	Europe

Taxonmaps

69479

Global distribution of 16S sequence AJ414656 (>99% sequence identity) for Methylocystis from Microbeatlas

Aquatic Samples	940
Soil Samples	493
Animal Samples	169
Plant Samples	43
Total Samples	1645

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
6861	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	ASM37284v1 assembly for Methylocystis rosea SV97	scaffold	GCA_000372845	1132444.3	2517287028	1132444	77.36

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
6861	Methylocystis rosea 16S ribosomal RNA, type strain SV97T	AJ414656	1438		1132444

GC content

6861

GC-content (mol%)62

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methylocystis rosea SV97
NCBI: GCA_000372845

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	93.30	no
125439	motility	BacteriaNetⓘ	yes	50.60	no
125439	gram_stain	BacteriaNetⓘ	negative	99.20	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methylocystis rosea SV97 SV97
NCBI: GCA_000372845.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	99.00	yes
125438	anaerobic	anaerobicⓘ	no	87.44	no
125438	aerobic	aerobicⓘ	yes	76.54	no
125438	spore-forming	spore-formingⓘ	no	82.61	no
125438	thermophilic	thermophileⓘ	no	97.50	yes
125438	flagellated	motile2+ⓘ	no	78.88	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Diversity and Habitat Preferences of Cultivated and Uncultivated Aerobic Methanotrophic Bacteria Evaluated Based on pmoA as Molecular Marker.	Knief C.	Front Microbiol	10.3389/fmicb.2015.01346	2015
Phylogeny	Methylocystis rosea sp. nov., a novel methanotrophic bacterium from Arctic wetland soil, Svalbard, Norway (78 degrees N).	Wartiainen I, Hestnes AG, McDonald IR, Svenning MM	Int J Syst Evol Microbiol	10.1099/ijs.0.63912-0	2006

References

#6861	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 17261
#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 )
#27906	IJSEM 541 2006 ( DOI 10.1099/ijs.0.63912-0 , PubMed 16514024 )
#31620	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 #27906
#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) .
#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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Methylocystis rosea (DSM 17261, ATCC BAA-1196, SV97)

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

Observation

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000372845

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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