Sulfurihydrogenibium azorense (DSM 15241, OCM 825, AZ-Fu1)

Name: Sulfurihydrogenibium azorense (DSM 15241, OCM 825, AZ-Fu1)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 519

Type strain

Strain Designation AZ-Fu1

Culture col. no. DSM 15241 OCM 825 AZ-Fu1

NCBI tax ID(s) 309806

Special Collections DSMZ Literature strains

History DSMZ <- P. Aguiar and A.-L. Reysenbach; AZ-Fu1

Links

Sulfurihydrogenibium azorense AZ-Fu1 is a Gram-negative, motile, rod-shaped bacterium that was isolated from mat and thermal water.

Gram-negative motile rod-shaped genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 130851

DSM 15241

@ref 20215

Last LPSN update 2026-05-29 08:25:03

Domain Bacteria

Phylum Aquificota

Class Aquificia

Order Aquificales

Family Hydrogenothermaceae

Genus Sulfurihydrogenibium

Species Sulfurihydrogenibium azorense

Full scientific name Sulfurihydrogenibium azorense Aguiar et al. 2004

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
29986	negative	0.9-2.0 µm	0.4-0.5 µm	rod-shaped
125439	negative				97.073
125438	negative				98.483

Pigmentation

29986

Productionno

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
5777	HYDROGEN-OXYDIZING MEDIUM (DSMZ Medium 1003)		Medium recipe at MediaDive	Name: HYDROGEN-OXYDIZING MEDIUM (DSMZ Medium 1003) Composition: MgSO4 x 7 H2O 7.0 g/l NaS2O3 2.0 g/l MES 1.95 g/l MgCl2 0.78 g/l KCl 0.48 g/l K2HPO4 x 3 H2O 0.3 g/l MgCl2 x H2O 0.2 g/l NH4Cl 0.2 g/l CaCl2 x 2 H2O 0.08 g/l Na-EDTA x 2 H2O 0.005 g/l CoCl2 x 6 H2O 0.0015 g/l MnCl2 x 4 H2O 0.001 g/l FeSO4 x 7 H2O 0.001 g/l ZnCl2 0.001 g/l AlCl3 x 6 H2O 0.0004 g/l Na-tungstate x 2 H2O 0.0003 g/l NiSO4 x 6 H2O 0.0002 g/l CuCl 0.0002 g/l Na2MoO4 x 2 H2O 0.0001 g/l Se-acid 0.0001 g/l HBO3 0.0001 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
5777	positive	growth	70
29986	positive	growth	50-73
29986	positive	optimum	68

Culture pH

@ref	Ability	Type	PH
29986	positive	optimum	6
29986	positive	growth	5.5-7

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate anaerobe	92.615

Spore formation

@ref	Spore formation	Confidence
29986
125439		99.484
125438		96.107

Halophily

@ref	Salt	Growth	Tested relation	Concentration
29986	NaCl	positive	growth	0.1 %
29986	NaCl	positive	optimum	0.1 %

Observation

29986

Observationaggregates in clumps

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29986	16947 ChEBI	citrate	+	carbon source
29986	28757 ChEBI	fructose	+	carbon source
29986	17234 ChEBI	glucose	+	carbon source
29986	24996 ChEBI	lactate	+	carbon source
29986	17306 ChEBI	maltose	+	carbon source
29986	37684 ChEBI	mannose	+	carbon source
29986	17272 ChEBI	propionate	+	carbon source
29986	30031 ChEBI	succinate	+	carbon source
29986	17992 ChEBI	sucrose	+	carbon source

Metabolite production

@ref	Chebi-ID	Metabolite	Production
29986	16136 ChEBI	hydrogen sulfide

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Environmental	#Aquatic

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
5777	mat and thermal water	Azores, S. Miguel, Furnas	Portugal	PRT	Europe

Taxonmaps

69479

Global distribution of 16S sequence AF528192 (>99% sequence identity) for Sulfurihydrogenibium azorense from Microbeatlas

Aquatic Samples	76
Soil Samples	1
Animal Samples	9
Plant Samples	3
Total Samples	89

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
5777	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	ASM2154v1 assembly for Sulfurihydrogenibium azorense Az-Fu1	complete	GCA_000021545	204536.4	643692050	204536	98.73

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
5777	Sulfurihydrogenibium azorense 16S ribosomal RNA gene, partial sequence	AF528192	1447		204536

GC content

5777

GC-content (mol%)33.6

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM2154v1 assembly for Sulfurihydrogenibium azorense Az-Fu1
BV-BRC: 204536.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate anaerobe	92.62	no
125439	gram_stain	BacteriaNetⓘ	negative	97.07	no
125439	motility	BacteriaNetⓘ	no	81.65	no
125439	spore_formation	BacteriaNetⓘ	no	99.48	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Sulfurihydrogenibium azorense Az-Fu1 Az-Fu1
NCBI: GCA_000021545.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.48	yes
125438	anaerobic	anaerobicⓘ	yes	59.22	no
125438	aerobic	aerobicⓘ	no	85.76	no
125438	spore-forming	spore-formingⓘ	no	96.11	yes
125438	thermophilic	thermophileⓘ	yes	72.51	yes
125438	flagellated	motile2+ⓘ	yes	50.10	no

Literature

References

#5777	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 15241
#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 )
#26353	IJSEM 33 2004 ( DOI 10.1099/ijs.0.02790-0 , PubMed 14742456 )
#29986	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 #26353
#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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