Name: Methanobacterium veterum MK4
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 6949

Type strain:

Species: Methanobacterium veterum

Strain Designation: MK4

Culture col. no.: DSM 19849, VKM B-2440

Strain history: <- V. Shcherbakova, Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Science, Pushchino, Russia; MK4 <- K. Krivushin; {2006}

NCBI tax ID(s): 408577 (species)

SI-ID 368575

DSM 19849, VKM B-2440

Special Collections

Section

When using BacDive for research please cite our paper

Methanobacterium veterum MK4 is an anaerobe, mesophilic, Gram-negative archaeon that was isolated from ancient permafrost sediments.

Gram-negative
rod-shaped
anaerobe
mesophilic
16S sequence
Archaea
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Archaea*
	Phylum	*Methanobacteriota*
	Class	*Methanobacteria*
	Order	*Methanobacteriales*
	Family	*Methanobacteriaceae*
	Genus	*Methanobacterium*
	Species	**Methanobacterium veterum**
	Full Scientific Name (LPSN)	Methanobacterium veterum Krivushin et al. 2010

Information on morphological and physiological properties Morphology

[Ref.: #29342]	Gram stain	negative
[Ref.: #125439]	Gram stain	negative Confidence in %96

[Ref.: #29342]	Cell length	5 µm

[Ref.: #29342]	Cell width	0.425 µm

[Ref.: #29342]	Cell shape	rod-shaped

[Ref.: #29342]	Motility	no
[Ref.: #125438]	Motility	no Confidence in %90.875

Information on culture and growth conditions Culture and growth conditions

[Ref.: #8288]

Culture medium

METHANOBACTERIUM ESPANOLAE MEDIUM (DSMZ Medium 506)

[Ref.: #8288]

Culture medium growth

[Ref.: #8288]

Culture medium link

Medium recipe at MediaDive

[Ref.: #8288]

Culture medium composition

expand / minimize

		Temperature
[Ref.: #8288]	growth	28 °C
[Ref.: #29342]	growth	10-46 °C
[Ref.: #29342]	optimum	28 °C

	pH	Kind of pH		pH
[Ref.: #29342]			optimum	7.3
[Ref.: #29342]			growth	5.2-9.4

Information on physiology and metabolism Physiology and metabolism

[Ref.: #8288]	Oxygen tolerance	anaerobe
[Ref.: #29342]	Oxygen tolerance	anaerobe

[Ref.: #29342]	Ability of spore formation	no
[Ref.: #125439]	Ability of spore formation	no Confidence in %98.2

	Halophily	Salt	Tested relation		Salt conc.
[Ref.: #29342]		NaCl		growth	0-0.3	%

	Metabolite utilization	Metabolite	Utilization activity	Kind of utilization tested
[Ref.: #29342]		acetate ChEBI	+	carbon source

Information on isolation source, the sampling and environmental conditions Isolation, sampling and environmental information

[Ref.: #8288]	Sample type/isolated from	ancient (3 million years old) permafrost sediments
[Ref.: #8288]	Geographic location (country and/or sea, region)	Kolyma Lowland (70° 06' N 154° 04' E)
[Ref.: #8288]	Country	Russia
[Ref.: #8288]	Country ISO 3 Code	RUS
[Ref.: #8288]	Continent	Asia
[Ref.: #8288]	Geographic location	70.1000°/154.0670°
* marker position based on {}

Isolation sources categories

#Environmental	#Terrestrial	#Permafrost
#Environmental	#Terrestrial	#Sediment

What are isolation sources categories?

Information on possible application of the strain and its possible interaction with e.g. potential hosts Safety information

[Ref.: #8288]

Biosafety level

Risk group (German classification)
According to TRBA 466

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #8288]	Methanobacterium veterum strain MK4 16S ribosomal RNA gene, partial sequence	EF016285	1347	GenBank ENA	408577 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Methanobacterium veterum MK4	GCA_000745485	scaffold	GenBank ENA	408577 tax ID	*
[Ref.: #66792]	Methanobacterium veterum MK4, DSM 19849	2582580744	draft	JGI IMG/M	408577 tax ID	*
[Ref.: #66792]	Methanobacterium veterum MK4	GCA_027110455	scaffold	GenBank ENA	408577 tax ID	*

[Ref.: #8288]	GC-content	33.8±0.3 mol%	thermal denaturation, midpoint method (Tm)
[Ref.: #29342]	GC-content	33.8 mol%

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanobacterium veterum MK4 NCBI: GCA_000745485.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	65.308	yes
[Ref.: #125438]	anaerobic	anaerobicⓘ	yes	88.763	yes
[Ref.: #125438]	aerobic	aerobicⓘ	no	85.617	yes
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	85.159	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	78.591	yes
[Ref.: #125438]	motile2+	flagellatedⓘ	no	90.875	yes

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanobacterium veterum MK4 NCBI: GCA_000745485
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	98.2
[Ref.: #125439]	BacteriaNet	motilityⓘ	no	74.8
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	96
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	anaerobe	74.8

Availability in culture collections External links

[Ref.: #8288]

Culture collection no.

DSM 19849, VKM B-2440

[Ref.: #76411]

SI-ID 368575

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Methanobacterium veterum sp. nov., from ancient Siberian permafrost.	Krivushin KV, Shcherbakova VA, Petrovskaya LE, Rivkina EM	Int J Syst Evol Microbiol	10.1099/ijs.0.011205-0	2009	*

References

#8288

Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 19849

#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 )

#29342

Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #25754 (see below)

#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) .

#76411

Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.: StrainInfo: A central database for resolving microbial strain identifiers . ( DOI 10.60712/SI-ID368575.1 )

#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 .

#25754

IJSEM 455 2010 ( DOI 10.1099/ijs.0.011205-0 , PubMed 19654368 )

* These data were automatically processed and therefore are not curated

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