Microbispora rosea (DSM 43837, ATCC 33325, CBS 305.61)

Name: Microbispora rosea (DSM 43837, ATCC 33325, CBS 305.61)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 16517

Type strain

Culture col. no. DSM 43837 ATCC 33325 CBS 305.61 IFO 14041 JCM 3023 10 more

NCBI tax ID(s) 58117 58118

Special Collections DSMZ JCM KCTC

History <- JCM (Microbispora diastatica) <- H. Nomomura, M-5 KCC A-0023 <-- H. Nonomura M-5.

Links

Microbispora rosea DSM 43837 is a mesophilic prokaryote that was isolated from soil.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 92879

ATCC 33325,NRRL B-2630,DSM 43837,IFO 14041,JCM 3023,CCRC 13321,CBS 305.61,KCTC 9312,AS 4.1342,NBRC 14041,BCRC 13321,NBRC 101785,CGMCC 4.1342

@ref 20215

Last LPSN update 2026-02-09 11:21:59

Domain Bacteria

Phylum Actinomycetota

Class Actinomycetes

Order Streptosporangiales

Family Streptosporangiaceae

Genus Microbispora

Species Microbispora rosea

Full scientific name Microbispora rosea Nonomura and Ohara 1957 (Approved Lists 1980)

Synonyms (6)

Microbispora karnatakensis
Microbispora parva
Microbispora amethystogenes
Microbispora diastatica
Microbispora indica
Microbispora chromogenes

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	positive	99.7
125438	positive	91.54

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
11321	GPHF-MEDIUM (DSMZ Medium 553)		Medium recipe at MediaDive	Name: GPHF-MEDIUM (DSMZ Medium 553) Composition: Agar 20.0 g/l Glucose 10.0 g/l Beef extract 5.0 g/l Yeast extract 5.0 g/l Casein peptone 5.0 g/l CaCl2 x 2 H2O 0.74 g/l Distilled water
11321	OAT FLAKES AGAR WITH YEAST EXTRACT (DSMZ Medium 248)		Medium recipe at MediaDive	Name: OAT FLAKES AGAR WITH YEAST EXTRACT (DSMZ Medium 248) Composition: Agar 20.0 g/l Oat flakes 20.0 g/l Yeast extract 2.0 g/l MnCl2 x 4 H2O 0.001 g/l FeSO4 x 7 H2O 0.001 g/l ZnSO4 x 7 H2O 0.001 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
11321	positive	growth	28	mesophilic
67770	positive	growth	28	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.8

Observation

67770

Observationquinones: MK-9(H₂), MK-9, MK-9(H₄)

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	molybdenum cofactor biosynthesis	100	9 of 9
66794	resorcinol degradation	100	2 of 2
66794	valine metabolism	100	9 of 9
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	ceramide biosynthesis	100	1 of 1
66794	suberin monomers biosynthesis	100	2 of 2
66794	enterobactin biosynthesis	100	3 of 3
66794	starch degradation	100	10 of 10
66794	biotin biosynthesis	100	4 of 4
66794	formaldehyde oxidation	100	3 of 3
66794	gallate degradation	100	5 of 5
66794	UDP-GlcNAc biosynthesis	100	3 of 3
66794	adipate degradation	100	2 of 2
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	sulfopterin metabolism	100	4 of 4
66794	hydrogen production	100	5 of 5
66794	vitamin K metabolism	100	5 of 5
66794	cellulose degradation	100	5 of 5
66794	3-chlorocatechol degradation	100	5 of 5
66794	acetate fermentation	100	4 of 4
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	folate polyglutamylation	100	1 of 1
66794	palmitate biosynthesis	100	22 of 22
66794	cardiolipin biosynthesis	100	7 of 7
66794	coenzyme A metabolism	100	4 of 4
66794	aerobactin biosynthesis	100	1 of 1
66794	ppGpp biosynthesis	100	4 of 4
66794	Entner Doudoroff pathway	100	10 of 10
66794	methylglyoxal degradation	100	5 of 5
66794	flavin biosynthesis	93.33	14 of 15
66794	tetrahydrofolate metabolism	92.86	13 of 14
66794	phenylalanine metabolism	92.31	12 of 13
66794	degradation of aromatic, nitrogen containing compounds	91.67	11 of 12
66794	pentose phosphate pathway	90.91	10 of 11
66794	phenol degradation	90	18 of 20
66794	4-hydroxyphenylacetate degradation	90	9 of 10
66794	threonine metabolism	90	9 of 10
66794	chorismate metabolism	88.89	8 of 9
66794	4-hydroxymandelate degradation	88.89	8 of 9
66794	aspartate and asparagine metabolism	88.89	8 of 9
66794	serine metabolism	88.89	8 of 9
66794	gluconeogenesis	87.5	7 of 8
66794	dTDPLrhamnose biosynthesis	87.5	7 of 8
66794	isoleucine metabolism	87.5	7 of 8
66794	C4 and CAM-carbon fixation	87.5	7 of 8
66794	photosynthesis	85.71	12 of 14
66794	heme metabolism	85.71	12 of 14
66794	glutamate and glutamine metabolism	85.71	24 of 28
66794	reductive acetyl coenzyme A pathway	85.71	6 of 7
66794	glutathione metabolism	85.71	12 of 14
66794	urea cycle	84.62	11 of 13
66794	tryptophan metabolism	84.21	32 of 38
66794	glycolate and glyoxylate degradation	83.33	5 of 6
66794	NAD metabolism	83.33	15 of 18
66794	histidine metabolism	82.76	24 of 29
66794	glycolysis	82.35	14 of 17
66794	metabolism of disaccharids	81.82	9 of 11
66794	purine metabolism	80.85	76 of 94
66794	myo-inositol biosynthesis	80	8 of 10
66794	factor 420 biosynthesis	80	4 of 5
66794	glycogen metabolism	80	4 of 5
66794	propionate fermentation	80	8 of 10
66794	phenylacetate degradation (aerobic)	80	4 of 5
66794	peptidoglycan biosynthesis	80	12 of 15
66794	ethylmalonyl-CoA pathway	80	4 of 5
66794	vitamin B12 metabolism	79.41	27 of 34
66794	citric acid cycle	78.57	11 of 14
66794	degradation of pentoses	78.57	22 of 28
66794	allantoin degradation	77.78	7 of 9
66794	d-mannose degradation	77.78	7 of 9
66794	CO2 fixation in Crenarchaeota	77.78	7 of 9
66794	vitamin B1 metabolism	76.92	10 of 13
66794	leucine metabolism	76.92	10 of 13
66794	non-pathway related	76.32	29 of 38
66794	degradation of sugar acids	76	19 of 25
66794	alanine metabolism	75.86	22 of 29
66794	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	75	6 of 8
66794	butanoate fermentation	75	3 of 4
66794	glycogen biosynthesis	75	3 of 4
66794	toluene degradation	75	3 of 4
66794	cyclohexanol degradation	75	3 of 4
66794	arginine metabolism	75	18 of 24
66794	oxidative phosphorylation	74.73	68 of 91
66794	lipid metabolism	74.19	23 of 31
66794	3-phenylpropionate degradation	73.33	11 of 15
66794	cholesterol biosynthesis	72.73	8 of 11
66794	propanol degradation	71.43	5 of 7
66794	ubiquinone biosynthesis	71.43	5 of 7
66794	tyrosine metabolism	71.43	10 of 14
66794	pyrimidine metabolism	71.11	32 of 45
66794	sulfate reduction	69.23	9 of 13
66794	isoprenoid biosynthesis	69.23	18 of 26
66794	androgen and estrogen metabolism	68.75	11 of 16
66794	methane metabolism	66.67	2 of 3
66794	acetyl CoA biosynthesis	66.67	2 of 3
66794	cyanate degradation	66.67	2 of 3
66794	degradation of hexoses	66.67	12 of 18
66794	octane oxidation	66.67	2 of 3
66794	selenocysteine biosynthesis	66.67	4 of 6
66794	IAA biosynthesis	66.67	2 of 3
66794	acetoin degradation	66.67	2 of 3
66794	L-lactaldehyde degradation	66.67	2 of 3
66794	methionine metabolism	65.38	17 of 26
66794	proline metabolism	63.64	7 of 11
66794	d-xylose degradation	63.64	7 of 11
66794	degradation of sugar alcohols	62.5	10 of 16
66794	ketogluconate metabolism	62.5	5 of 8
66794	lysine metabolism	61.9	26 of 42
66794	phosphatidylethanolamine bioynthesis	61.54	8 of 13
66794	cysteine metabolism	61.11	11 of 18
66794	polyamine pathway	60.87	14 of 23
66794	D-cycloserine biosynthesis	60	3 of 5
66794	metabolism of amino sugars and derivatives	60	3 of 5
66794	carotenoid biosynthesis	59.09	13 of 22
66794	aclacinomycin biosynthesis	57.14	4 of 7
66794	nitrate assimilation	55.56	5 of 9
66794	CMP-KDO biosynthesis	50	2 of 4
66794	aminopropanol phosphate biosynthesis	50	1 of 2
66794	phenylmercury acetate degradation	50	1 of 2
66794	mannosylglycerate biosynthesis	50	1 of 2
66794	ribulose monophosphate pathway	50	1 of 2
66794	lactate fermentation	50	2 of 4
66794	glycine metabolism	50	5 of 10
66794	ethanol fermentation	50	1 of 2
66794	kanosamine biosynthesis II	50	1 of 2
66794	quinate degradation	50	1 of 2
66794	grixazone biosynthesis	50	1 of 2
66794	catecholamine biosynthesis	50	2 of 4
66794	bile acid biosynthesis, neutral pathway	47.06	8 of 17
66794	ascorbate metabolism	45.45	10 of 22
66794	lipid A biosynthesis	44.44	4 of 9
66794	arachidonic acid metabolism	44.44	8 of 18
66794	benzoyl-CoA degradation	42.86	3 of 7
66794	coenzyme M biosynthesis	40	4 of 10
66794	bacilysin biosynthesis	40	2 of 5
66794	lipoate biosynthesis	40	2 of 5
66794	arachidonate biosynthesis	40	2 of 5
66794	glycine betaine biosynthesis	40	2 of 5
66794	chlorophyll metabolism	38.89	7 of 18
66794	phenylpropanoid biosynthesis	38.46	5 of 13
66794	carnitine metabolism	37.5	3 of 8
66794	vitamin B6 metabolism	36.36	4 of 11
66794	(5R)-carbapenem carboxylate biosynthesis	33.33	1 of 3
66794	pantothenate biosynthesis	33.33	2 of 6
66794	sphingosine metabolism	33.33	2 of 6
66794	sulfoquinovose degradation	33.33	1 of 3
66794	methanogenesis from CO2	33.33	4 of 12
66794	mevalonate metabolism	28.57	2 of 7
66794	dolichyl-diphosphooligosaccharide biosynthesis	27.27	3 of 11
66794	daunorubicin biosynthesis	22.22	2 of 9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil

Isolation

@ref	Sample type
11321	soil
67770	Soil

Taxonmaps

69479

Global distribution of 16S sequence U48990 (>99% sequence identity) for Microbispora from Microbeatlas

Aquatic Samples	10
Soil Samples	327
Animal Samples	14
Plant Samples	113
Total Samples	464

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
11321	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
67770	ASM71846v2 assembly for Microbispora rosea NRRL B-2630	contig	GCA_000718465	58118.3	2772190709	58118

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Microbispora diastatica 16S rRNA gene	U48990	1413		58118

GC content

@ref	GC-content (mol%)	Method
67770	71	thermal denaturation, midpoint method (Tm)
67770	68.9	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Microbispora rosea subsp. rosea NRRL B-2630
NCBI: GCA_000718465

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	53.50	no
125439	motility	BacteriaNetⓘ	no	82.90	no
125439	gram_stain	BacteriaNetⓘ	positive	99.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.80	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Microbispora rosea subsp. rosea NRRL B-2630
NCBI: GCA_000718465.2

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	91.54	no
125438	anaerobic	anaerobicⓘ	no	86.84	no
125438	aerobic	aerobicⓘ	yes	89.83	no
125438	spore-forming	spore-formingⓘ	yes	86.83	no
125438	thermophilic	thermophileⓘ	no	95.50	yes
125438	flagellated	motile2+ⓘ	no	86.00	no

Literature

References

#11321	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 43837
#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 )
#66794	Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, Dietmar Schomburg: BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49: D498 - D508 2020 ( DOI 10.1093/nar/gkaa1025 , PubMed 33211880 )
#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 .
#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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