Methanosarcina mazei (DSM 3318, ATCC 43340, OCM 98)

Name: Methanosarcina mazei (DSM 3318, ATCC 43340, OCM 98)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7099

Type strain

Strain Designation C16

Culture col. no. DSM 3318 ATCC 43340 OCM 98 C16

NCBI tax ID(s) 2209

Special Collections DSMZ

History <- K.H. Blotevogel, C16

Links

Methanosarcina mazei C16 is an anaerobe, mesophilic prokaryote that was isolated from marine sediment.

anaerobe mesophilic genome sequence

Name and taxonomic classification

SI-ID 132872

ATCC 43340,DSM 3318,BCRC 16171,CCRC 16171

@ref 20215

Last LPSN update 2026-02-09 11:19:23

Domain Archaea

Phylum Methanobacteriota

Class Methanosarcinia

Order Methanosarcinales

Family Methanosarcinaceae

Genus Methanosarcina

Species Methanosarcina mazei

Full scientific name Methanosarcina mazei corrig. (Barker 1936) Mah and Kuhn 1984

Synonyms (4)

Methanosarcina frisia
Methanococcus frisius
Methanosarcina mazeii
Methanococcus mazei

BacDive ID	Other strains from **Methanosarcina mazei** (13)	Type strain
7096	M. mazei S-6, DSM 2053, ATCC 43572, OCM 26, DSM 108209 (type strain)
7097	M. mazei Z-558, DSM 2244, OCM 92
7098	M. mazei MC3, DSM 2907, OCM 192
7100	M. mazei G1, DSM 3338, OCM 87
7101	M. mazei Gö1, DSM 3647, ATCC BAA-159, JCM 11833, ...
7102	M. mazei DSM 4556, ATCC 43573, OCM 34
7103	M. mazei T18, DSM 6300
7104	M. mazei Equi, DSM 7058, OCM 61
7105	M. mazei Gö1, DSM 7222
7106	M. mazei TMA, DSM 9195, JCM 9314, NBRC 101201
7107	M. mazei SarPi, DSM 10132
24166	M. mazei O1M9701, DSM 27785, OCM 748
164152	M. mazei JCM 31898, VKM B-2370

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	91.4

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
1355	METHANOCOCCOIDES MEDIUM (DSMZ Medium 141c)		Medium recipe at MediaDive	Name: METHANOCOCCOIDES MEDIUM (DSMZ Medium 141c; with strain-specific modifications) Composition: NaHCO3 4.89237 g/l MgCl2 x 6 H2O 3.91389 g/l Methanol 3.87476 g/l Yeast extract 1.95695 g/l Trypticase peptone 1.95695 g/l Na-acetate 0.978474 g/l Na2S x 9 H2O 0.489237 g/l L-Cysteine HCl x H2O 0.489237 g/l KCl 0.332681 g/l NH4Cl 0.244618 g/l K2HPO4 0.136986 g/l MgSO4 x 7 H2O 0.0293542 g/l Nitrilotriacetic acid 0.0146771 g/l NaCl 0.00978474 g/l MnSO4 x H2O 0.00489237 g/l Fe(NH4)2(SO4)2 x 6 H2O 0.00195695 g/l CoSO4 x 7 H2O 0.00176125 g/l ZnSO4 x 7 H2O 0.00176125 g/l CaCl2 x 2 H2O 0.000978474 g/l FeSO4 x 7 H2O 0.000978474 g/l Sodium resazurin 0.000489237 g/l NiCl2 x 6 H2O 0.000293542 g/l AlK(SO4)2 x 12 H2O 0.000195695 g/l CuSO4 x 5 H2O 9.78474e-05 g/l Pyridoxine hydrochloride 9.78474e-05 g/l Na2MoO4 x 2 H2O 9.78474e-05 g/l H3BO3 9.78474e-05 g/l (DL)-alpha-Lipoic acid 4.89237e-05 g/l Thiamine HCl 4.89237e-05 g/l Riboflavin 4.89237e-05 g/l Nicotinic acid 4.89237e-05 g/l Calcium D-(+)-pantothenate 4.89237e-05 g/l p-Aminobenzoic acid 4.89237e-05 g/l Biotin 1.95695e-05 g/l Folic acid 1.95695e-05 g/l Na2WO4 x 2 H2O 3.91389e-06 g/l Na2SeO3 x 5 H2O 2.93542e-06 g/l Vitamin B12 9.78474e-07 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
1355	positive	growth	35	mesophilic

Physiology and metabolism

Oxygen tolerance

1355

Oxygen toleranceanaerobe

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Marine
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
1355	marine sediment	Greetsiel, North Sea	Germany	DEU	Europe

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
1355	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	ASM97024v1 assembly for Methanosarcina mazei C16	complete	GCA_000970245	1434113.4	2636415905	1434113	98.41

GC content

1355

GC-content (mol%)38.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanosarcina mazei C16
NCBI: GCA_000970245

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	89.30	no
125439	motility	BacteriaNetⓘ	no	78.60	no
125439	gram_stain	BacteriaNetⓘ	negative	91.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	85.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanosarcina mazei C16 C16
NCBI: GCA_000970245.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	66.17	no
125438	anaerobic	anaerobicⓘ	yes	86.75	yes
125438	aerobic	aerobicⓘ	no	91.29	yes
125438	spore-forming	spore-formingⓘ	no	82.84	no
125438	thermophilic	thermophileⓘ	no	84.31	yes
125438	flagellated	motile2+ⓘ	no	84.21	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Enzymology	Localization of Methyl-Coenzyme M reductase as metabolic marker for diverse methanogenic Archaea.	Wrede C, Walbaum U, Ducki A, Heieren I, Hoppert M.	Archaea	10.1155/2013/920241	2013
Genetics	A genetic system for Archaea of the genus Methanosarcina: liposome-mediated transformation and construction of shuttle vectors.	Metcalf WW, Zhang JK, Apolinario E, Sowers KR, Wolfe RS.	Proc Natl Acad Sci U S A	10.1073/pnas.94.6.2626	1997
	Reduction in Methane Emissions From Acidified Dairy Slurry Is Related to Inhibition of Methanosarcina Species.	Habtewold J, Gordon R, Sokolov V, VanderZaag A, Wagner-Riddle C, Dunfield K.	Front Microbiol	10.3389/fmicb.2018.02806	2018
Metabolism	Targeting Bacteria and Methanogens To Understand the Role of Residual Slurry as an Inoculant in Stored Liquid Dairy Manure.	Habtewold J, Gordon R, Sokolov V, VanderZaag A, Wagner-Riddle C, Dunfield K.	Appl Environ Microbiol	10.1128/aem.02830-17	2018

References

#1355	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 3318
#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) .
#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 mazei (DSM 3318, ATCC 43340, OCM 98)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000970245

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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