Pseudothermotoga elfii (DSM 9442, ATCC 51869, NBRC 107921)

Name: Pseudothermotoga elfii (DSM 9442, ATCC 51869, NBRC 107921)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 17065

Type strain

Culture col. no. DSM 9442 ATCC 51869 NBRC 107921 NCIMB 13424 SEBR 6459

NCBI tax ID(s) 1123383 38322

Special Collections DSMZ

History DSMZ <- G. Ravot, Université de Provence, Marseille, France; SEBR 6459 <- G. Ravot {1994}

Links

Pseudothermotoga elfii DSM 9442 is an anaerobe bacterium that was isolated from oil-producing well.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 43220

NBRC 107921,ATCC 51869,DSM 9442,NCIMB 13424

@ref 20215

Last LPSN update 2026-05-29 08:28:55

Domain Bacteria

Phylum Thermotogota

Class Thermotogae

Order Thermotogales

Family Thermotogaceae

Genus Pseudothermotoga

Species Pseudothermotoga elfii

Full scientific name Pseudothermotoga elfii (Ravot et al. 1995) Bhandari and Gupta 2014

Synonyms (1)

Thermotoga elfii

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	95.099

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
3574	PSEUDOTHERMOTOGA ELFII MEDIUM (DSMZ Medium 664)		Medium recipe at MediaDive	Name: PSEUDOTHERMOTOGA ELFII MEDIUM (DSMZ Medium 664) Composition: Na2S2O3 x 5 H2O 4.9505 g/l Trypticase peptone 4.9505 g/l Yeast extract 4.9505 g/l D-Glucose 3.9604 g/l Na2CO3 1.48515 g/l NH4Cl 0.990099 g/l L-Cysteine HCl x H2O 0.49505 g/l Na2S x 9 H2O 0.49505 g/l Na-acetate 0.49505 g/l K2HPO4 0.29703 g/l KH2PO4 0.29703 g/l MgCl2 x 6 H2O 0.19802 g/l KCl 0.0990099 g/l MgSO4 x 7 H2O 0.029703 g/l Nitrilotriacetic acid 0.0148515 g/l NaCl 0.00990099 g/l MnSO4 x H2O 0.00495049 g/l ZnSO4 x 7 H2O 0.00178218 g/l CoSO4 x 7 H2O 0.00178218 g/l FeSO4 x 7 H2O 0.000990099 g/l CaCl2 x 2 H2O 0.000990099 g/l Sodium resazurin 0.00049505 g/l NiCl2 x 6 H2O 0.00029703 g/l AlK(SO4)2 x 12 H2O 0.00019802 g/l Na2MoO4 x 2 H2O 9.90099e-05 g/l H3BO3 9.90099e-05 g/l CuSO4 x 5 H2O 9.90099e-05 g/l Na2WO4 x 2 H2O 3.9604e-06 g/l Na2SeO3 x 5 H2O 2.9703e-06 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
3574	positive	growth	65

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
3574	anaerobe
125438	anaerobe	93.826

Spore formation

@ref	Spore formation	Confidence
125439		96.641

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Industrial	#Engineered product
#Engineered	#Industrial	#Oil reservoir

Isolation

@ref	Sample type	Geographic location
3574	oil-producing well	Africa

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
3574	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	ASM50408v1 assembly for Pseudothermotoga elfii DSM 9442 = NBRC 107921	complete	GCA_000504085	1219083.7	2558309118	1123383	98.1
66792	ASM62134v1 assembly for Pseudothermotoga elfii DSM 9442 = NBRC 107921	scaffold	GCA_000621345	1123383.3	2558309103	1123383	79.01

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
3574	T.elfii 16S rRNA gene	X80790	1519		38322

GC content

@ref	GC-content (mol%)	Method
3574	39.6	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM50408v1 assembly for Pseudothermotoga elfii DSM 9442 = NBRC 107921
BV-BRC: 1219083.7

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	68.25	no
125439	gram_stain	BacteriaNetⓘ	negative	95.10	no
125439	motility	BacteriaNetⓘ	no	71.47	no
125439	spore_formation	BacteriaNetⓘ	no	96.64	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Pseudothermotoga elfii DSM 9442 = NBRC 107921 NBRC 107921
NCBI: GCA_000504085.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	67.59	no
125438	anaerobic	anaerobicⓘ	yes	93.83	yes
125438	aerobic	aerobicⓘ	no	97.64	yes
125438	spore-forming	spore-formingⓘ	no	75.31	no
125438	thermophilic	thermophileⓘ	yes	81.06	yes
125438	flagellated	motile2+ⓘ	yes	55.39	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Enzymology	Characterization of the GH13 and GH57 glycogen branching enzymes from Petrotoga mobilis SJ95 and potential role in glycogen biosynthesis.	Zhang X, Leemhuis H, van der Maarel MJEC.	PLoS One	10.1371/journal.pone.0219844	2019
Genetics	Identical sequences found in distant genomes reveal frequent horizontal transfer across the bacterial domain.	Sheinman M, Arkhipova K, Arndt PF, Dutilh BE, Hermsen R, Massip F.	Elife	10.7554/elife.62719	2021
	Responses to the Hydrostatic Pressure of Surface and Subsurface Strains of Pseudothermotoga elfii Revealing the Piezophilic Nature of the Strain Originating From an Oil-Producing Well.	Roumagnac M, Pradel N, Bartoli M, Garel M, Jones AA, Armougom F, Fenouil R, Tamburini C, Ollivier B, Summers ZM, Dolla A	Front Microbiol	10.3389/fmicb.2020.588771	2020
Phylogeny	Thermotoga elfii sp. nov., a novel thermophilic bacterium from an African oil-producing well.	Ravot G, Magot M, Fardeau ML, Patel BK, Prensier G, Egan A, Garcia JL, Ollivier B	Int J Syst Bacteriol	10.1099/00207713-45-2-308	1995

References

#3574	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 9442
#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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Pseudothermotoga elfii (DSM 9442, ATCC 51869, NBRC 107921)

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_000504085

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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