Methanocaldococcus fervens (DSM 4213, JCM 15782, AG86)

Name: Methanocaldococcus fervens (DSM 4213, JCM 15782, AG86)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 6984

Type strain

Strain Designation AG86

Culture col. no. DSM 4213 JCM 15782 AG86

NCBI tax ID(s) 573064 83171

Special Collections DSMZ JCM

History <- H. Zhao and M.P. Bryant, AG86 DSM 4213 <-- H. Zhao and M. P. Bryant AG86.

Links

Methanocaldococcus fervens AG86 is an anaerobe archaeon that was isolated from core sample from a deep sea hydrothermal vent.

anaerobe genome sequence 16S sequence Archaea

Name and taxonomic classification

SI-ID 47812

DSM 4213,JCM 15782

@ref 20215

Last LPSN update 2026-02-09 11:19:21

Domain Archaea

Phylum Methanobacteriota

Class Methanococci

Order Methanococcales

Family Methanocaldococcaceae

Genus Methanocaldococcus

Species Methanocaldococcus fervens

Full scientific name Methanocaldococcus fervens (Jeanthon et al. 1999) Whitman 2002

Synonyms (1)

Methanococcus fervens

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
1616	METHANOCALDOCOCCUS MEDIUM (DSMZ Medium 282)		Medium recipe at MediaDive	Name: METHANOCALDOCOCCUS MEDIUM (DSMZ Medium 282) Composition: MgCl2 x 6 H2O 4.05138 g/l NaHCO3 0.988142 g/l Na2S x 9 H2O 0.494071 g/l L-Cysteine HCl x H2O 0.494071 g/l KCl 0.326087 g/l NH4Cl 0.247036 g/l K2HPO4 0.13834 g/l MgSO4 x 7 H2O 0.0296443 g/l Nitrilotriacetic acid 0.0148221 g/l NaCl 0.00988142 g/l Fe(NH4)2(SO4)2 x 6 H2O 0.00988142 g/l MnSO4 x H2O 0.00494071 g/l ZnSO4 x 7 H2O 0.00177866 g/l CoSO4 x 7 H2O 0.00177866 g/l CaCl2 x 2 H2O 0.000988142 g/l FeSO4 x 7 H2O 0.000988142 g/l Sodium resazurin 0.000494071 g/l NiCl2 x 6 H2O 0.000296443 g/l AlK(SO4)2 x 12 H2O 0.000197628 g/l Na2MoO4 x 2 H2O 9.88142e-05 g/l CuSO4 x 5 H2O 9.88142e-05 g/l H3BO3 9.88142e-05 g/l Pyridoxine hydrochloride 9.88142e-05 g/l Thiamine HCl 4.94071e-05 g/l (DL)-alpha-Lipoic acid 4.94071e-05 g/l p-Aminobenzoic acid 4.94071e-05 g/l Riboflavin 4.94071e-05 g/l Nicotinic acid 4.94071e-05 g/l Calcium D-(+)-pantothenate 4.94071e-05 g/l Biotin 1.97628e-05 g/l Folic acid 1.97628e-05 g/l Na2WO4 x 2 H2O 3.95257e-06 g/l Na2SeO3 x 5 H2O 2.96443e-06 g/l Vitamin B12 9.88142e-07 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
1616	positive	growth	85
67770	positive	growth	85

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
1616	anaerobe
125438	anaerobe	90.718
125439	anaerobe	99.4

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Hydrothermal vent
#Environmental	#Aquatic	#Marine
#Environmental	#Terrestrial	#Core sample
#Condition	#Thermophilic (>45°C)	-

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
1616	core sample from a deep sea hydrothermal vent	Gulf of California, Guayamas Basin	USA	USA	North America
67770	Hydrothermal vent core sample from Guaymas Basin, at a depth of 2,003 m	Gulf of California	USA	USA	North America

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
1616	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	ASM2398v1 assembly for Methanocaldococcus fervens AG86	complete	GCA_000023985	573064.11	644736385	573064	99.03

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Methanococcus fervens 16S ribosomal RNA gene, partial sequence	AF056938	1398		573064

GC content

@ref	GC-content (mol%)	Method
1616	33.0	thermal denaturation, midpoint method (Tm)
67770	33	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanocaldococcus fervens AG86
IMG: 644736385

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	87.80	no
125439	motility	BacteriaNetⓘ	yes	66.70	no
125439	gram_stain	BacteriaNetⓘ	variable	81.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	99.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanocaldococcus fervens AG86 AG86
NCBI: GCA_000023985.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	63.31	no
125438	anaerobic	anaerobicⓘ	yes	90.72	yes
125438	aerobic	aerobicⓘ	no	91.91	yes
125438	spore-forming	spore-formingⓘ	no	89.04	no
125438	thermophilic	thermophileⓘ	yes	64.52	yes
125438	flagellated	motile2+ⓘ	no	82.88	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Regulation of the methanogenesis pathways by hydrogen at transcriptomic level in time.	Szuhaj M, Kakuk B, Wirth R, Rakhely G, Kovacs KL, Bagi Z.	Appl Microbiol Biotechnol	10.1007/s00253-023-12700-3	2023
	RNAsoft: A suite of RNA secondary structure prediction and design software tools.	Andronescu M, Aguirre-Hernandez R, Condon A, Hoos HH.	Nucleic Acids Res	10.1093/nar/gkg612	2003
Phylogeny	Methanococcus vulcanius sp. nov., a novel hyperthermophilic methanogen isolated from East Pacific Rise, and identification of Methanococcus sp. DSM 4213T as Methanococcus fervens sp. nov.	Jeanthon C, L'Haridon S, Reysenbach AL, Corre E, Vernet M, Messner P, Sleytr UB, Prieur D	Int J Syst Bacteriol	10.1099/00207713-49-2-583	1999

References

#1616	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 4213
#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) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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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Methanocaldococcus fervens (DSM 4213, JCM 15782, AG86)

Name and taxonomic classification

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_000023985

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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