Azospirillum halopraeferens (DSM 3675, ATCC 43709, LMG 7108)

Name: Azospirillum halopraeferens (DSM 3675, ATCC 43709, LMG 7108)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 13981

Type strain

Strain Designation AU4

Culture col. no. DSM 3675 ATCC 43709 LMG 7108 CCUG 45018 AU4

NCBI tax ID(s) 1121033 34010

Special Collections DSMZ CCUG

Links

Azospirillum halopraeferens AU4 is a mesophilic prokaryote that was isolated from roots of Kallar grass Kunth).

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 1052

LMG 7108,DSM 3675,CCUG 45018,ATCC 43709,NCIMB 13139,BCRC 14284,CCRC 14284

@ref 20215

Last LPSN update 2025-12-16 09:50:14

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Azospirillaceae

Genus Azospirillum

Species Azospirillum halopraeferens

Full scientific name Azospirillum halopraeferens Reinhold et al. 1987

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	97.833

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
1452	HETEROTROPHIC MEDIUM H3P (DSMZ Medium 428)		Medium recipe at MediaDive	Name: HETEROTROPHIC MEDIUM H3P (DSMZ Medium 428) Composition: Agar 19.802 g/l Na2HPO4 x 2 H2O 2.87129 g/l KH2PO4 2.27723 g/l D-Glucose 1.9802 g/l Na-lactate 0.990099 g/l Na-pyruvate 0.990099 g/l D-Mannitol 0.990099 g/l DL-Malate 0.990098 g/l Na-acetate 0.990098 g/l Yeast extract 0.990098 g/l NH4Cl 0.990098 g/l Disodium succinate 0.990098 g/l MgSO4 x 7 H2O 0.495049 g/l Ferric ammonium citrate 0.049505 g/l CaCl2 x 2 H2O 0.00990098 g/l NaVO3 x H2O 0.00495049 g/l Thiamine-HCl x 2 H2O 0.00247525 g/l Nicotinic acid 0.00247525 g/l Pyridoxine hydrochloride 0.00247525 g/l Calcium pantothenate 0.00247525 g/l H3BO3 0.00148515 g/l CoCl2 x 6 H2O 0.000990099 g/l Riboflavin 0.00049505 g/l ZnSO4 x 7 H2O 0.00049505 g/l MnCl2 x 4 H2O 0.000148515 g/l Na2MoO4 x 2 H2O 0.000148515 g/l NiCl2 x 6 H2O 9.90099e-05 g/l CuCl2 x 2 H2O 4.95049e-05 g/l Vitamin B12 4.95049e-05 g/l Folic acid 9.90099e-06 g/l Biotin 4.9505e-06 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
1452	positive	growth	37	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.3

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Plants	#Herbaceous plants (Grass,Crops)
#Host Body-Site	#Plant	#Root (Rhizome)

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
1452	roots of Kallar grass (Leptochloa fusca (Linn.) Kunth)	Leptochloa fusca	Punjab	Pakistan	PAK	Asia

Taxonmaps

69479

Global distribution of 16S sequence GU256439 (>99% sequence identity) for Azospirillum halopraeferens subclade from Microbeatlas

Aquatic Samples	8
Soil Samples	1
Animal Samples	3
Plant Samples	2
Total Samples	14

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
1452	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	ASM42962v1 assembly for Azospirillum halopraeferens DSM 3675	scaffold	GCA_000429625	1121033.3	2522572158	1121033	67.55

16S sequences

@ref	Description	Accession	Length	NCBI tax ID
20218	A.halopraeferens (Au4) 16S rRNA gene	X79731	1323	34010
20218	Azospirillum halopraeferens strain DSM 3675 16S ribosomal RNA gene, partial sequence	GU256439	1393	34010
20218	A.halopraeferens (DSM 3675) 16S ribosomal RNA	Z29618	1417	34010

GC content

1452

GC-content (mol%)68.1

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Azospirillum halopraeferens DSM 3675
NCBI: GCA_000429625

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	89.80	no
125439	motility	BacteriaNetⓘ	yes	62.70	no
125439	gram_stain	BacteriaNetⓘ	negative	89.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Azospirillum halopraeferens DSM 3675 DSM 3675
NCBI: GCA_000429625.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.83	no
125438	anaerobic	anaerobicⓘ	no	85.98	no
125438	aerobic	aerobicⓘ	yes	69.59	no
125438	spore-forming	spore-formingⓘ	no	78.14	no
125438	thermophilic	thermophileⓘ	no	97.00	yes
125438	flagellated	motile2+ⓘ	yes	75.21	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Complete Genome Sequence of a Novel Azospirillum Strain TA Isolated from Western Siberia Chernevaya Taiga Soil.	Rayko M, Kravchenko I, Lapidus A.	Microorganisms	10.3390/microorganisms12122599	2024
	Prevalence and Specificity of Chemoreceptor Profiles in Plant-Associated Bacteria.	Sanchis-Lopez C, Cerna-Vargas JP, Santamaria-Hernando S, Ramos C, Krell T, Rodriguez-Palenzuela P, Lopez-Solanilla E, Huerta-Cepas J, Rodriguez-Herva JJ.	mSystems	10.1128/msystems.00951-21	2021
Metabolism	Identification and isolation of genes involved in poly(beta-hydroxybutyrate) biosynthesis in Azospirillum brasilense and characterization of a phbC mutant.	Kadouri D, Burdman S, Jurkevitch E, Okon Y.	Appl Environ Microbiol	10.1128/aem.68.6.2943-2949.2002	2002

References

#1452	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 3675
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#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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Azospirillum halopraeferens (DSM 3675, ATCC 43709, LMG 7108)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000429625

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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