Methanosarcina siciliae (DSM 6564, OCM 210, HI350)

Name: Methanosarcina siciliae (DSM 6564, OCM 210, HI350)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7084

Type strain

Strain Designation HI350

Culture col. no. DSM 6564 OCM 210 HI350

NCBI tax ID(s) 38027

Special Collections DSMZ

History <- OCM <- S. Ni, strain HI350

Links

Methanosarcina siciliae HI350 is an anaerobe, mesophilic prokaryote that was isolated from production water from an off-shore oil well.

anaerobe mesophilic genome sequence

Name and taxonomic classification

SI-ID 48237

DSM 6564

@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 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
7083	M. siciliae T4/M, DSM 3028, OCM 156 (type strain)
7085	M. siciliae C2J, DSM 11916, OCM 653

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	96

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
2731	METHANOCOCCOIDES MEDIUM (DSMZ Medium 141c)		Medium recipe at MediaDive	Name: METHANOCOCCOIDES MEDIUM (DSMZ Medium 141c) Composition: NaHCO3 4.89237 g/l Trimethylammonium chloride 4.89237 g/l MgCl2 x 6 H2O 3.91389 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 H3BO3 9.78474e-05 g/l Na2MoO4 x 2 H2O 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
2731	positive	growth	37	mesophilic

Physiology and metabolism

Oxygen tolerance

2731

Oxygen toleranceanaerobe

Spore formation

@ref	Spore formation	Confidence
125439		96.4

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Industrial	#Oil reservoir
#Environmental	#Aquatic	-

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
2731	production water from an off-shore oil well	Gulf of Mexico	USA	USA	North America

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
2731	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	ASM97012v1 assembly for Methanosarcina siciliae HI350	complete	GCA_000970125	1434119.4	2645727536	1434119	96.95

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanosarcina siciliae HI350
IMG: 2645727536

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	96.40	no
125439	motility	BacteriaNetⓘ	no	79.40	no
125439	gram_stain	BacteriaNetⓘ	negative	96.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	89.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanosarcina siciliae HI350 HI350
NCBI: GCA_000970125.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	63.37	no
125438	anaerobic	anaerobicⓘ	yes	86.51	no
125438	aerobic	aerobicⓘ	no	90.17	yes
125438	spore-forming	spore-formingⓘ	no	81.24	no
125438	thermophilic	thermophileⓘ	no	88.61	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
Metabolism	Potassium extrusion by the moderately halophilic and alkaliphilic methanogen methanolobus taylorii GS-16 and homeostasis of cytosolic pH.	Ni S, Boone JE, Boone DR.	J Bacteriol	10.1128/jb.176.23.7274-7279.1994	1994
Phylogeny	Transfer of Methanolobus siciliae to the genus Methanosarcina, naming it Methanosarcina siciliae, and emendation of the genus Methanosarcina.	Ni S, Woese CR, Aldrich HC, Boone DR	Int J Syst Bacteriol	10.1099/00207713-44-2-357	1994
Phylogeny	Isolation and characterization of a dimethyl sulfide-degrading methanogen, Methanolobus siciliae HI350, from an oil well, characterization of M. siciliae T4/MT, and emendation of M. siciliae.	Ni SS, Boone DR	Int J Syst Bacteriol	10.1099/00207713-41-3-410	1991

References

#2731	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 6564
#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 siciliae (DSM 6564, OCM 210, HI350)

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

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000970125

Genome-based predictions

Predictions

Literature

Literature

References

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