Azotobacter vinelandii (DSM 279, CCUG 51007, 567)

Name: Azotobacter vinelandii (DSM 279, CCUG 51007, 567)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 12739

Type strain

Strain Designation 567

Culture col. no. DSM 279 CCUG 51007 567

NCBI tax ID(s) 354

Special Collections DSMZ CCUG

History <- H. Stolp, 567

Links

Azotobacter vinelandii 567 is a bacterium of the family Pseudomonadaceae.

genome sequence Bacteria

Name and taxonomic classification

SI-ID 47133

DSM 279,CCUG 51007

@ref 20215

Last LPSN update 2026-02-09 11:20:55

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Pseudomonadales

Family Pseudomonadaceae

Genus Azotobacter

Species Azotobacter vinelandii

Full scientific name Azotobacter vinelandii Lipman 1903 (Approved Lists 1980)

BacDive ID	Other strains from **Azotobacter vinelandii** (16)	Type strain
12749	A. vinelandii 16, DSM 2289, ATCC 478, VKM B-1617, JCM 21475, ... (type strain)
12740	A. vinelandii 73, DSM 332
12741	A. vinelandii OP, DSM 366, ATCC 13705
12742	A. vinelandii Kg 2, DSM 382
12743	A. vinelandii 1610, DSM 389, IMG 1610
12744	A. vinelandii W2, DSM 390
12745	A. vinelandii 5R, DSM 395
12746	A. vinelandii DSM 399, RH Rh6, RH 6
12747	A. vinelandii DSM 576, NCIB 9068
12748	A. vinelandii 135, DSM 720, ATCC 17962, BKM B-547, VKM ...
12750	A. vinelandii VI, DSM 2290, ATCC 7496
12751	A. vinelandii 1484, DSM 85, IMG 1484
12752	A. vinelandii 1484, Claus 1484T, DSM 86
12753	A. vinelandii 3a, Johnstone 3a, DSM 87, ATCC 12837, NCIB ...
12754	A. vinelandii 93-541b, DSM 13529
162709	A. vinelandii JCM 20040, ATCC 9046, CECT 9203, IAM 1078

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	99.8
125439		93.4
125438	negative	99

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
18	DIAZOTROPHIC MEDIUM (RBA) (DSMZ Medium 441)		Medium recipe at MediaDive	Name: DIAZOTROPHIC MEDIUM (RBA) (DSMZ Medium 441) Composition: Agar 14.881 g/l DL-Malate 1.98413 g/l D-Glucose 1.98413 g/l D-Mannitol 1.98413 g/l Na-pyruvate 0.992063 g/l Disodium succinate 0.992063 g/l K2HPO4 0.892857 g/l KH2PO4 0.0992065 g/l NaCl 0.0992065 g/l CaCl2 x 2 H2O 0.0992065 g/l MgSO4 x 7 H2O 0.0992065 g/l Yeast extract 0.0496032 g/l FeSO4 x 7 H2O 0.00992065 g/l MnSO4 x H2O 0.00496032 g/l NaVO3 x H2O 0.00496032 g/l Nicotinic acid 0.00248016 g/l Pyridoxine hydrochloride 0.00248016 g/l Calcium pantothenate 0.00248016 g/l Thiamine-HCl x 2 H2O 0.00248016 g/l H3BO3 0.000892857 g/l CoCl2 x 6 H2O 0.000595238 g/l Riboflavin 0.000496032 g/l ZnSO4 x 7 H2O 0.000297619 g/l MnCl2 x 4 H2O 8.92857e-05 g/l Na2MoO4 x 2 H2O 8.92857e-05 g/l NiCl2 x 6 H2O 5.95238e-05 g/l Vitamin B12 4.96032e-05 g/l CuCl2 x 2 H2O 2.97619e-05 g/l Folic acid 9.92063e-06 g/l Biotin 4.96032e-06 g/l Distilled water
18	AZOTOBACTER MEDIUM (DSMZ Medium 3)		Medium recipe at MediaDive	Name: AZOTOBACTER MEDIUM (DSMZ Medium 3) Composition: Agar 15.0 g/l CaCO3 5.0 g/l Glucose 5.0 g/l Mannitol 5.0 g/l K2HPO4 0.9 g/l KH2PO4 0.1 g/l MgSO4 x 7 H2O 0.1 g/l CaCl2 x 2 H2O 0.1 g/l FeSO4 x 7 H2O 0.01 g/l Na2MoO4 x 2 H2O 0.005 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
18	positive	growth	30

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		99.1

Isolation, sampling and environmental information

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
18	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	IMG-taxon 2597490339 annotated assembly for Azotobacter vinelandii DSM 279	scaffold	GCA_900119555	354.5	2597490339	354	39.32

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Azotobacter vinelandii DSM 279
NCBI: GCA_900119555

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.10	no
125439	motility	BacteriaNetⓘ	yes	93.40	no
125439	gram_stain	BacteriaNetⓘ	negative	99.80	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	71.10	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Azotobacter vinelandii DSM 279
NCBI: GCA_900119555.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	99.00	no
125438	anaerobic	anaerobicⓘ	no	93.52	no
125438	aerobic	aerobicⓘ	yes	79.07	no
125438	spore-forming	spore-formingⓘ	no	88.43	no
125438	thermophilic	thermophileⓘ	no	94.80	no
125438	flagellated	motile2+ⓘ	yes	87.77	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Comparative genomics and metabolomics reveal phytohormone production, nutrient acquisition, and osmotic stress tolerance in Azotobacter chroococcum W5.	Elakkya M, Gonzalez-Salazar LA, Lopez-Reyes K, Rebelo-Romao I, Sousa A, Godde V, Niehaus K, Thenappan DP, Vilchez JI, Paul S, Licona-Cassani C.	Front Microbiol	10.3389/fmicb.2025.1626016	2025
	Genomic and phenotypic characterization of Pseudomonas sp. GOM7, a novel marine bacterial species with antimicrobial activity against multidrug-resistant Staphylococcus aureus.	Romero-Gonzalez LE, Rojas-Vargas J, Muriel-Millan LF, Bustos-Martinez J, Bustamante VH, Pardo-Lopez L.	PLoS One	10.1371/journal.pone.0288504	2023
	Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ.	Campos DT, Zuniga C, Passi A, Del Toro J, Tibocha-Bonilla JD, Zepeda A, Betenbaugh MJ, Zengler K.	Metab Eng Commun	10.1016/j.mec.2020.e00132	2020

References

#18	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 279
#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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Azotobacter vinelandii (DSM 279, CCUG 51007, 567)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Spore formation

Isolation, sampling and environmental information

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_900119555

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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