Methanothermobacter defluvii (DSM 7466, ATCC 51443, VKM B-1962)

Name: Methanothermobacter defluvii (DSM 7466, ATCC 51443, VKM B-1962)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 6896

Type strain

Strain Designation ADZ

Culture col. no. DSM 7466 ATCC 51443 VKM B-1962 ADZ

NCBI tax ID(s) 49339

Special Collections DSMZ

Links

Methanothermobacter defluvii ADZ is an anaerobe, thermophilic prokaryote that was isolated from sludge, metacrilic wastes digester.

anaerobe thermophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 42986

ATCC 51443,DSM 7466,VKM B-1962

@ref 20215

Last LPSN update 2025-12-16 09:48:26

Domain Methanobacteriati

Phylum Methanobacteriota

Class Methanobacteria

Order Methanobacteriales

Family Methanobacteriaceae

Genus Methanothermobacter

Species Methanothermobacter defluvii

Full scientific name Methanothermobacter defluvii (Kotelnikova et al. 1994) Boone 2002

Synonyms (1)

Methanobacterium defluvii

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
3171	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)	Range
3171	positive	growth	60	thermophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance
3171	anaerobe
125439	anaerobe

Spore formation

@ref	Spore formation	Confidence
125438		91.938

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Engineered	#Biodegradation
#Engineered	#Waste
#Environmental	#Terrestrial

Isolation

@ref	Sample type	Country	Country ISO 3 Code
3171	sludge, metacrilic wastes digester	Russia	RUS

Taxonmaps

69479

Global distribution of 16S sequence X99046 (>99% sequence identity) for Methanothermobacter from Microbeatlas

Aquatic Samples	47
Soil Samples	3
Animal Samples	8
Plant Samples	5
Total Samples	63

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
3171	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	ASM338575v1 assembly for Methanothermobacter defluvii DSM 7466	scaffold	GCA_003385755	49339.3	2757320366	49339	75.64

Genome browser: GCA_003385755

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
3171	M.defluvii 16S rRNA gene	X99046	1445		49339

GC content

3171

GC-content (mol%)62.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methanothermobacter defluvii DSM 7466
NCBI: GCA_003385755

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	79.70	no
125439	motility	BacteriaNetⓘ	no	71.20	no
125439	gram_stain	BacteriaNetⓘ	variable	57.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	99.70	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methanothermobacter defluvii DSM 7466
NCBI: GCA_003385755.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	52.23	no
125438	anaerobic	anaerobicⓘ	yes	89.15	no
125438	aerobic	aerobicⓘ	no	88.68	yes
125438	spore-forming	spore-formingⓘ	no	91.94	no
125438	thermophilic	thermophileⓘ	yes	62.46	yes
125438	flagellated	motile2+ⓘ	no	89.00	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Bio-hydrogen and bio-methane potentials of skim latex serum in batch thermophilic two-stage anaerobic digestion.	Jariyaboon R, O-Thong S, Kongjan P.	Bioresour Technol	10.1016/j.biortech.2015.09.006	2015
Metabolism	Performance and microbial community analysis of two-stage process with extreme thermophilic hydrogen and thermophilic methane production from hydrolysate in UASB reactors.	Kongjan P, O-Thong S, Angelidaki I.	Bioresour Technol	10.1016/j.biortech.2010.12.009	2011
Phenotype	Phenotypic and genomic characterization of Methanothermobacter wolfeii strain BSEL, a CO₂-capturing archaeon with minimal nutrient requirements.	Ale Enriquez F, Ahring BK.	Appl Environ Microbiol	10.1128/aem.00268-24	2024
Metabolism	Comparative genomics of DNA-binding transcription factors in archaeal and bacterial organisms.	Martinez-Liu L, Hernandez-Guerrero R, Rivera-Gomez N, Martinez-Nunez MA, Escobar-Turriza P, Peeters E, Perez-Rueda E.	PLoS One	10.1371/journal.pone.0254025	2021
Phylogeny	Comparison of 16S rRNA gene sequences of genus Methanobrevibacter.	Dighe AS, Jangid K, Gonzalez JM, Pidiyar VJ, Patole MS, Ranade DR, Shouche YS.	BMC Microbiol	10.1186/1471-2180-4-20	2004

References

#3171	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 7466
#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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Methanothermobacter defluvii (DSM 7466, ATCC 51443, VKM B-1962)

Name and taxonomic classification

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_003385755

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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