Cite this strain

Geotalea toluenoxydans (DSM 19350, JCM 15764, TMJ1)

Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 5806

Type strain

Strain Designation TMJ1

Culture col. no. DSM 19350 JCM 15764 TMJ1

NCBI tax ID(s) 1294260 421624

Special Collections DSMZ JCM Literature strains

Links

Name and taxonomic classification

Geotalea toluenoxydans TMJ1 is an anaerobe, mesophilic, Gram-negative bacterium that was isolated from sludge of a monitoring well at a tar-oil contaminated aquifer.

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

SI-ID 405747

DSM 19350

Last LPSN update 2024-12-09 10:43:46

Domain Bacteria

Phylum Pseudomonadota

Class Deltaproteobacteria

Order Desulfuromonadales

Family Geobacteraceae

Genus Geotalea

Species Geotalea toluenoxydans

Full scientific name Geotalea toluenoxydans (Kunapuli et al. 2010) Waite et al. 2020

Synonyms (1)

Geobacter toluenoxydans

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape
29004	negative	2.95 µm	0.4 µm	rod-shaped
125439	negative
125438	negative

Colony morphology

8065

Incubation period3-7 days

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link
8065	GEOANAEROBACTER MEDIUM (DSMZ Medium 838)		Medium recipe provided by DSMZ

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
8065	positive	growth	30	mesophilic
29004	positive	growth	25-32	mesophilic
29004	positive	optimum	28.5	mesophilic
67770	positive	growth	30	mesophilic

Culture pH

@ref	Ability	Type	PH
29004	positive	optimum	6.8
29004	positive	growth	6.6-7.5

Physiology and metabolism

Spore formation

@ref	Spore formation
29004
125439

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29004	17272 ChEBI	propionate	+	carbon source
29004	15361 ChEBI	pyruvate	+	carbon source

Oxygen tolerance

@ref	Oxygen tolerance
8065	anaerobe
29004	anaerobe

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Contamination	#Oil (Fuel)
#Engineered	#Industrial	#Oil reservoir
#Environmental	#Aquatic	-
#Environmental	#Terrestrial	#Mud (Sludge)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
8065	sludge of a monitoring well at a tar-oil contaminated aquifer	Stuttgart	Germany	DEU	Europe
67770	Sludge of a monitoring well at a tar-oil-contaminated aquifer	Stuttgart	Germany	DEU	Europe

Taxonmaps

69479

Global distribution of 16S sequence EU711072 (>99% sequence identity) for Geobacter from Microbeatlas

Aquatic Samples	505
Soil Samples	714
Animal Samples	11
Plant Samples	30
Total Samples	1260

Safety information

Risk assessment

@ref	Biosafety level	Biosafety level comment
8065	1	Risk group (German classification) According to TRBA 466

Sequence information

GC content

@ref	GC-content (mol%)	Method
8065	54.4	high performance liquid chromatography (HPLC)
29004	54.4

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
8065	Geobacter toluenoxydans strain TMJ1 16S ribosomal RNA gene, partial sequence	EU711072	1440		1294260

Genome sequences

@ref	Description	Accession	Assembly level	Database	NCBI tax ID
66792	Geobacter toluenoxydans JCM 15764	1294260.3	wgs	PATRIC	1294260
66792	Geobacter toluenoxydans JCM 15764	2728369703	draft	JGI IMG/M	1294260
67770	Geotalea toluenoxydans JCM 15764	GCA_001311985	contig		1294260

Genome-based predictions

Predictions

Predictions from Diaspora project based on:
Geotalea toluenoxydans JCM 15764
NCBI: GCA_001311985

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	97.20	no
125439	motility	BacteriaNetⓘ	no	61.40	no
125439	gram_stain	BacteriaNetⓘ	negative	98.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	84.70	no

Predictions from Diaspora project based on:
Geobacter toluenoxydans JCM 15764
PATRIC: 1294260.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.47	no
125438	anaerobic	anaerobicⓘ	yes	75.03	no
125438	spore-forming	spore-formingⓘ	no	87.19	no
125438	aerobic	aerobicⓘ	no	81.52	no
125438	thermophilic	thermophileⓘ	no	86.80	yes
125438	flagellated	motile2+ⓘ	yes	68.41	no

External links

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Desulfitobacterium aromaticivorans sp. nov. and Geobacter toluenoxydans sp. nov., iron-reducing bacteria capable of anaerobic degradation of monoaromatic hydrocarbons.	Kunapuli U, Jahn MK, Lueders T, Geyer R, Heipieper HJ, Meckenstock RU	Int J Syst Evol Microbiol	10.1099/ijs.0.003525-0	2009

Straininfo Link

@ref	Straininfo	Culture collections
75285	405747	DSM 19350

Culture collection no.

Culture collection no.
DSM 19350
JCM 15764
TMJ1

References

#8065	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 19350
#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 )
#29004	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 #25437 (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) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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 .
#75285	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-ID405747.1 )
#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 .

Rate our new design

Content

About us