Methanoculleus marisnigri (DSM 1498, ATCC 35101, OCM 56)

Name: Methanoculleus marisnigri (DSM 1498, ATCC 35101, OCM 56)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7020

Type strain

Strain Designation JR 1, JR1

Culture col. no. DSM 1498 ATCC 35101 OCM 56 JR 1 JR1

NCBI tax ID(s) 2198

Special Collections DSMZ

History <- W. E. Balch, Univ. Illinois, Urbana, USA; JR 1 <- J. A. Romesser and R. S. Wolfe;

Links

Methanoculleus marisnigri JR 1 is an anaerobe archaeon that was isolated from marine sediment.

anaerobe genome sequence 16S sequence Archaea

Name and taxonomic classification

SI-ID 40871

ATCC 35101,DSM 1498

@ref 20215

Last LPSN update 2026-02-09 11:19:22

Domain Archaea

Phylum Methanobacteriota

Class "Methanomicrobia"

Order Methanomicrobiales

Family Methanomicrobiaceae

Genus Methanoculleus

Species Methanoculleus marisnigri

Full scientific name Methanoculleus marisnigri (Romesser et al. 1981) Maestrojuán et al. 1990

Synonyms (1)

Methanogenium marisnigri

BacDive ID	Other strains from **Methanoculleus marisnigri** (1)	Type strain
7021	M. marisnigri AN8, DSM 4552, OCM 51

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	97

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
795	METHANOGENIUM MEDIUM (H2/CO2) (DSMZ Medium 141)		Medium recipe at MediaDive	Name: METHANOGENIUM MEDIUM (H2/CO2) (DSMZ Medium 141) Composition: NaHCO3 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 FeSO4 x 7 H2O 0.000978474 g/l CaCl2 x 2 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 Nicotinic acid 4.89237e-05 g/l Riboflavin 4.89237e-05 g/l (DL)-alpha-Lipoic acid 4.89237e-05 g/l Thiamine HCl 4.89237e-05 g/l Calcium D-(+)-pantothenate 4.89237e-05 g/l p-Aminobenzoic acid 4.89237e-05 g/l Folic acid 1.95695e-05 g/l Biotin 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)
795	positive	growth	28

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
795	anaerobe
125439	obligate anaerobe	98.7

Spore formation

@ref	Spore formation	Confidence
125439		97.6

Isolation, sampling and environmental information

Isolation source categories

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

Isolation

@ref	Sample type	Geographic location
795	marine sediment	Black Sea

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
795	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	ASM1582v1 assembly for Methanoculleus marisnigri JR1	complete	GCA_000015825	368407.15	640069318	368407	99.24

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Methanoculleus marisnigri strain DSM 1498 16S ribosomal RNA gene, partial sequence	M59134	1466		2198

GC content

795

GC-content (mol%)61.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanoculleus marisnigri JR1
NCBI: GCA_000015825

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	97.60	no
125439	motility	BacteriaNetⓘ	no	57.80	no
125439	gram_stain	BacteriaNetⓘ	negative	97.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate anaerobe	98.70	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanoculleus marisnigri JR1 JR1
NCBI: GCA_000015825.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	70.76	no
125438	anaerobic	anaerobicⓘ	yes	87.83	yes
125438	aerobic	aerobicⓘ	no	90.24	yes
125438	spore-forming	spore-formingⓘ	no	88.00	no
125438	thermophilic	thermophileⓘ	no	79.75	no
125438	flagellated	motile2+ⓘ	no	84.38	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Extracellular electron uptake in Methanosarcinales is independent of multiheme c-type cytochromes.	Yee MO, Rotaru AE.	Sci Rep	10.1038/s41598-019-57206-z	2020
	RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties.	Chakoory O, Comtet-Marre S, Peyret P.	NAR Genom Bioinform	10.1093/nargab/lqac070	2022
	Complete genome sequence of Methanoculleus marisnigri Romesser et al. 1981 type strain JR1.	Anderson IJ, Sieprawska-Lupa M, Lapidus A, Nolan M, Copeland A, Glavina Del Rio T, Tice H, Dalin E, Barry K, Saunders E, Han C, Brettin T, Detter JC, Bruce D, Mikhailova N, Pitluck S, Hauser L, Land M, Lucas S, Richardson P, Whitman WB, Kyrpides NC.	Stand Genomic Sci	10.4056/sigs.32535	2009
Genetics	Genomic characterization of methanomicrobiales reveals three classes of methanogens.	Anderson I, Ulrich LE, Lupa B, Susanti D, Porat I, Hooper SD, Lykidis A, Sieprawska-Lupa M, Dharmarajan L, Goltsman E, Lapidus A, Saunders E, Han C, Land M, Lucas S, Mukhopadhyay B, Whitman WB, Woese C, Bristow J, Kyrpides N.	PLoS One	10.1371/journal.pone.0005797	2009

References

#795	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 1498
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#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 )

Rate our new design

Content

About us

Methanoculleus marisnigri (DSM 1498, ATCC 35101, OCM 56)

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_000015825

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

Help

Social Media