Methanosarcina siciliae (DSM 3028, OCM 156, T4/M)

Name: Methanosarcina siciliae (DSM 3028, OCM 156, T4/M)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7083

Type strain

Strain Designation T4/M

Culture col. no. DSM 3028 OCM 156 T4/M

NCBI tax ID(s) 38027

Special Collections DSMZ

History DSMZ <- K.O. Stetter, T4/M

Links

Methanosarcina siciliae T4/M is an anaerobe archaeon that was isolated from lake sediment.

anaerobe genome sequence 16S sequence Archaea

Name and taxonomic classification

SI-ID 47685

DSM 3028,ATCC BAA-931

@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 siciliae

Full scientific name Methanosarcina siciliae (Stetter and König 1989) Ni et al. 1994

Synonyms (1)

Methanolobus siciliae

BacDive ID	Other strains from **Methanosarcina siciliae** (2)	Type strain
7084	M. siciliae HI350, DSM 6564, OCM 210
7085	M. siciliae C2J, DSM 11916, OCM 653

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	97.714

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
1274	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)
1274	positive	growth	37

Physiology and metabolism

Oxygen tolerance

1274

Oxygen toleranceanaerobe

Spore formation

@ref	Spore formation	Confidence
125439		99.653

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Lake (large)
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
1274	lake sediment	Sicily, Lakes of Marinello	Italy	ITA	Europe

Taxonmaps

69479

Global distribution of 16S sequence FR733698 (>99% sequence identity) for Methanosarcina siciliae from Microbeatlas

Aquatic Samples	149
Soil Samples	112
Animal Samples	15
Plant Samples	10
Total Samples	286

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
1274	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	ASM97008v1 assembly for Methanosarcina siciliae T4/M	complete	GCA_000970085	1434120.4	2651869866	1434120	96.35

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
1274	Methanosarcina siciliae partial 16S rRNA gene, type strain DSM3028T	FR733698	1472		38027

GC content

1274

GC-content (mol%)41.5

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM97008v1 assembly for Methanosarcina siciliae T4/M
BV-BRC: 1434120.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	79.19	no
125439	gram_stain	BacteriaNetⓘ	negative	97.71	no
125439	motility	BacteriaNetⓘ	no	87.35	no
125439	spore_formation	BacteriaNetⓘ	no	99.65	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanosarcina siciliae T4/M T4/M
NCBI: GCA_000970085.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	61.92	no
125438	anaerobic	anaerobicⓘ	yes	84.03	yes
125438	aerobic	aerobicⓘ	no	89.10	yes
125438	spore-forming	spore-formingⓘ	no	81.09	no
125438	thermophilic	thermophileⓘ	no	89.11	yes
125438	flagellated	motile2+ⓘ	no	81.88	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Genetic basis for metabolism of methylated sulfur compounds in Methanosarcina species.	Fu H, Metcalf WW.	J Bacteriol	10.1128/jb.02605-14	2015
	Methanosarcina Spherical Virus, a Novel Archaeal Lytic Virus Targeting Methanosarcina Strains.	Weidenbach K, Nickel L, Neve H, Alkhnbashi OS, Kunzel S, Kupczok A, Bauersachs T, Cassidy L, Tholey A, Backofen R, Schmitz RA.	J Virol	10.1128/jvi.00955-17	2017
Phylogeny	Application of denaturing high-performance liquid chromatography in microbial ecology: fermentor sludge, compost, and soil community profiling.	Wagner AO, Malin C, Illmer P.	Appl Environ Microbiol	10.1128/aem.01411-08	2009
	Methanogenic conversion of 3-s-methylmercaptopropionate to 3-mercaptopropionate.	van der Maarel M, Jansen M, Hansen TA	Appl Environ Microbiol	10.1128/aem.61.1.48-51.1995	1995
Phylogeny	Methanosarcina subterranea sp. nov., a methanogenic archaeon isolated from a deep subsurface diatomaceous shale formation.	Shimizu S, Ueno A, Naganuma T, Kaneko K	Int J Syst Evol Microbiol	10.1099/ijs.0.000072	2015

References

#1274	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 3028
#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 siciliae (DSM 3028, OCM 156, T4/M)

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_000970085

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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