Methanosarcina vacuolata (DSM 1232, ATCC 35090, OCM 85)

Name: Methanosarcina vacuolata (DSM 1232, ATCC 35090, OCM 85)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7126

Type strain

Strain Designation Z-761

Culture col. no. DSM 1232 ATCC 35090 OCM 85 Z-761

NCBI tax ID(s) 2215

Special Collections DSMZ

History DSMZ <- G.A. Zavarzin, Z-761 <- T.N. Zhilina (Methanosarcina sp. biotype 2)

Links

Methanosarcina vacuolata Z-761 is an anaerobe archaeon that was isolated from sludge of a methane tank.

anaerobe genome sequence 16S sequence Archaea

Name and taxonomic classification

SI-ID 40866

ATCC 35090,DSM 1232,BCRC 16172,CCRC 16172

@ref 20215

Last LPSN update 2026-05-29 08:26:34

Domain Archaea

Phylum Methanobacteriota

Class Methanosarcinia

Order Methanosarcinales

Family Methanosarcinaceae

Genus Methanosarcina

Species Methanosarcina vacuolata

Full scientific name Methanosarcina vacuolata Zhilina and Zavarzin 1987

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	93.638

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
544	METHANOSARCINA BARKERI MEDIUM (DSMZ Medium 120a)		Medium recipe at MediaDive	Name: METHANOSARCINA BARKERI MEDIUM (DSMZ Medium 120a) Composition: Methanol 15.4688 g/l NaCl 2.19727 g/l Yeast extract 1.95312 g/l Casitone 1.95312 g/l NaHCO3 0.830078 g/l NH4Cl 0.488281 g/l MgSO4 x 7 H2O 0.488281 g/l K2HPO4 0.341797 g/l Na2S x 9 H2O 0.292969 g/l L-Cysteine HCl x H2O 0.292969 g/l CaCl2 x 2 H2O 0.244141 g/l KH2PO4 0.224609 g/l HCl 0.00244141 g/l FeSO4 x 7 H2O 0.00195312 g/l FeCl2 x 4 H2O 0.00146484 g/l Sodium resazurin 0.000488281 g/l CoCl2 x 6 H2O 0.000185547 g/l Pyridoxine hydrochloride 9.76563e-05 g/l MnCl2 x 4 H2O 9.76563e-05 g/l ZnCl2 6.83594e-05 g/l Thiamine HCl 4.88281e-05 g/l Riboflavin 4.88281e-05 g/l Calcium D-(+)-pantothenate 4.88281e-05 g/l Nicotinic acid 4.88281e-05 g/l p-Aminobenzoic acid 4.88281e-05 g/l (DL)-alpha-Lipoic acid 4.88281e-05 g/l Na2MoO4 x 2 H2O 3.51562e-05 g/l NiCl2 x 6 H2O 2.34375e-05 g/l Folic acid 1.95312e-05 g/l Biotin 1.95312e-05 g/l H3BO3 5.85938e-06 g/l CuCl2 x 2 H2O 1.95313e-06 g/l Vitamin B12 9.76563e-07 g/l H2SO4 Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
544	positive	growth	37

Physiology and metabolism

Oxygen tolerance

544

Oxygen toleranceanaerobe

Spore formation

@ref	Spore formation	Confidence
125439		98.333

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Built environment	#Container (Reservoir)
#Environmental	#Terrestrial	#Mud (Sludge)

Isolation

@ref	Sample type	Country	Continent
544	sludge of a methane tank	USSR	Asia

Taxonmaps

69479

Global distribution of 16S sequence FR733661 (>99% sequence identity) for Methanosarcina from Microbeatlas

Aquatic Samples	645
Soil Samples	267
Animal Samples	33
Plant Samples	59
Total Samples	1004

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
544	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	ASM96990v1 assembly for Methanosarcina vacuolata Z-761	complete	GCA_000969905	1434123.4	2648501188	1434123	96.41

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
544	Methanosarcina vacuolata partial 16S rRNA gene, type strain DSM1232T	FR733661	1473		2215

GC content

544

GC-content (mol%)36.3

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM96990v1 assembly for Methanosarcina vacuolata Z-761
BV-BRC: 1434123.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	85.62	no
125439	gram_stain	BacteriaNetⓘ	negative	93.64	no
125439	motility	BacteriaNetⓘ	no	87.76	no
125439	spore_formation	BacteriaNetⓘ	no	98.33	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanosarcina vacuolata Z-761 Z-761
NCBI: GCA_000969905.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	67.66	no
125438	anaerobic	anaerobicⓘ	yes	82.91	yes
125438	aerobic	aerobicⓘ	no	87.41	yes
125438	spore-forming	spore-formingⓘ	no	83.11	no
125438	thermophilic	thermophileⓘ	no	86.52	yes
125438	flagellated	motile2+ⓘ	no	82.21	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Differentiation of methanosaeta concilii and methanosarcina barkeri in anaerobic mesophilic granular sludge by fluorescent In situ hybridization and confocal scanning laser microscopy	Rocheleau S, Greer CW, Lawrence JR, Cantin C, Laramee L, Guiot SR.	Appl Environ Microbiol	10.1128/aem.65.5.2222-2229.1999	1999
Phylogeny	Methanosarcina spelaei sp. nov., a methanogenic archaeon isolated from a floating biofilm of a subsurface sulphurous lake.	Ganzert L, Schirmack J, Alawi M, Mangelsdorf K, Sand W, Hillebrand-Voiculescu A, Wagner D	Int J Syst Evol Microbiol	10.1099/ijs.0.064956-0	2014

References

#544	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 1232
#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 )
#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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Methanosarcina vacuolata (DSM 1232, ATCC 35090, OCM 85)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000969905

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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