Granulicella arctica (DSM 23128, ATCC BAA-1858, MP5ACTX2)

Name: Granulicella arctica (DSM 23128, ATCC BAA-1858, MP5ACTX2)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 22898

Type strain

Strain Designation MP5ACTX2

Culture col. no. DSM 23128 ATCC BAA-1858 MP5ACTX2

NCBI tax ID(s) 940613

Special Collections DSMZ Literature strains

Links

Granulicella arctica MP5ACTX2 is an aerobe, psychrophilic, Gram-negative prokaryote that was isolated from tundra soil.

Gram-negative rod-shaped aerobe psychrophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 406335

ATCC BAA-1858,DSM 23128

@ref 20215

Last LPSN update 2025-12-16 09:52:31

Domain Pseudomonadati

Phylum Acidobacteriota

Class Terriglobia

Order Terriglobales

Family Acidobacteriaceae

Genus Granulicella

Species Granulicella arctica

Full scientific name Granulicella arctica Männistö et al. 2012

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility	Confidence
30254	negative	1.1 µm	0.5 µm	rod-shaped
125438	negative					90.769

Colony morphology

19167

Incubation period3-7 days

Pigmentation

30254

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
19167	R2A MEDIUM (DSMZ Medium 830)		Medium recipe at MediaDive	Name: R2A MEDIUM (DSMZ Medium 830; with strain-specific modifications) Composition: Agar 5.0 g/l Casamino acids 0.5 g/l Starch 0.5 g/l Glucose 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
19167	positive	growth	20	psychrophilic
30254	positive	growth	4-28
30254	positive	optimum	22.5	psychrophilic

Culture pH

@ref	Ability	Type	PH	PH range
30254	positive	optimum	5
30254	positive	growth	3.5-6.5	acidophile

Physiology and metabolism

Oxygen tolerance

30254

Oxygen toleranceaerobe

Halophily

@ref	Salt	Growth	Tested relation	Concentration
30254	NaCl	positive	growth	<1 %

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30254	17057 ChEBI	cellobiose	+	carbon source
30254	4853 ChEBI	esculin	+	hydrolysis
30254	28757 ChEBI	fructose	+	carbon source
30254	28260 ChEBI	galactose	+	carbon source
30254	17234 ChEBI	glucose	+	carbon source
30254	17716 ChEBI	lactose	+	carbon source
30254	17306 ChEBI	maltose	+	carbon source
30254	37684 ChEBI	mannose	+	carbon source
30254	506227 ChEBI	N-acetylglucosamine	+	carbon source
30254	16634 ChEBI	raffinose	+	carbon source
30254	17814 ChEBI	salicin	+	carbon source
30254	17992 ChEBI	sucrose	+	carbon source
30254	27082 ChEBI	trehalose	+	carbon source
30254	18222 ChEBI	xylose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
30254	acid phosphatase	+	3.1.3.2
30254	alkaline phosphatase	+	3.1.3.1
30254	alpha-galactosidase	+	3.2.1.22
30254	catalase	+	1.11.1.6

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil
#Climate	#Cold	#Tundra
#Environmental	#Terrestrial	#Tundra

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
19167	tundra soil	Lapland, Kilpisjärvi	Finland	FIN	Europe

Taxonmaps

69479

Global distribution of 16S sequence HQ687089 (>99% sequence identity) for Granulicella arctica from Microbeatlas

Aquatic Samples
Soil Samples	10
Animal Samples
Plant Samples
Total Samples	10

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
19167	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	ASM2568560v1 assembly for Granulicella arctica DSM 23128	contig	GCA_025685605	940613.4	8047311272	940613	70.96

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
19167	Granulicella arctica strain MP5ACTX2 16S ribosomal RNA gene, partial sequence	HQ687089	1490		940613

GC content

@ref	GC-content (mol%)	Method
19167	57.5	high performance liquid chromatography (HPLC)
30254	57.5

Genome-based predictions

Predictions

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Granulicella arctica DSM 23128
NCBI: GCA_025685605.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	90.77	yes
125438	anaerobic	anaerobicⓘ	no	94.17	no
125438	aerobic	aerobicⓘ	yes	85.05	yes
125438	spore-forming	spore-formingⓘ	no	80.46	no
125438	thermophilic	thermophileⓘ	no	95.04	yes
125438	flagellated	motile2+ⓘ	no	56.11	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Granulicella arctica sp. nov., Granulicella mallensis sp. nov., Granulicella tundricola sp. nov. and Granulicella sapmiensis sp. nov., novel acidobacteria from tundra soil.	Mannisto MK, Rawat S, Starovoytov V, Haggblom MM	Int J Syst Evol Microbiol	10.1099/ijs.0.031864-0	2011

References

#19167	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 23128
#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 )
#30254	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 #26596 (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) .
#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 )
#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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Granulicella arctica (DSM 23128, ATCC BAA-1858, MP5ACTX2)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Halophily

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_025685605

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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