Name: Gemmatimonas aurantiaca T-27
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 5788

Type strain:

Species: Gemmatimonas aurantiaca

Strain Designation: T-27

Culture col. no.: DSM 14586, JCM 11422, NBRC 100505

Strain history: S. Hanada T-27.

NCBI tax ID(s): 379066 (strain), 173480 (species)

SI-ID 111713

JCM 11422, DSM 14586, NBRC 100505

Special Collections

DSMZ
JCM

Section

When using BacDive for research please cite our paper

Gemmatimonas aurantiaca T-27 is a mesophilic, Gram-negative bacterium that was isolated from sludge from laboratory reactor.

Gram-negative
mesophilic
16S sequence
Bacteria
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Bacteria*
	Phylum	*Gemmatimonadota*
	Class	*Gemmatimonadia*
	Order	*Gemmatimonadales*
	Family	*Gemmatimonadaceae*
	Genus	*Gemmatimonas*
	Species	**Gemmatimonas aurantiaca**
	Full Scientific Name (LPSN)	Gemmatimonas aurantiaca Zhang et al. 2003

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %93.885

Information on culture and growth conditions Culture and growth conditions

[Ref.: #5395]

Culture medium

R2A MEDIUM (DSMZ Medium 830)

[Ref.: #5395]

Culture medium growth

[Ref.: #5395]

Culture medium link

Medium recipe at MediaDive

[Ref.: #5395]

Culture medium composition

expand / minimize

	Temperatures	Kind of temperature		Temperature
[Ref.: #5395]			growth	30 °C
[Ref.: #67770]			growth	30 °C

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125439]

Oxygen tolerance

obligate aerobe Confidence in %97.9

[Ref.: #67770]

Observation

quinones: MK-9

	Pathway	Enzyme coverage	Annotated reactions
	Pathway annotation	Computationally determined by
[Ref.: #66794]	adipate degradation	100	2 of 2
[Ref.: #66794]	sulfopterin metabolism	100	4 of 4
[Ref.: #66794]	coenzyme A metabolism	100	4 of 4
[Ref.: #66794]	cis-vaccenate biosynthesis	100	2 of 2
[Ref.: #66794]	gluconeogenesis	100	8 of 8
[Ref.: #66794]	aspartate and asparagine metabolism	100	9 of 9
[Ref.: #66794]	palmitate biosynthesis	100	22 of 22
[Ref.: #66794]	1,4-dihydroxy-6-naphthoate biosynthesis	100	6 of 6
[Ref.: #66794]	enterobactin biosynthesis	100	3 of 3
[Ref.: #66794]	kanosamine biosynthesis II	100	2 of 2
[Ref.: #66794]	cardiolipin biosynthesis	100	7 of 7
[Ref.: #66794]	denitrification	100	2 of 2
[Ref.: #66794]	biotin biosynthesis	100	4 of 4
[Ref.: #66794]	anapleurotic synthesis of oxalacetate	100	1 of 1
[Ref.: #66794]	ppGpp biosynthesis	100	4 of 4
[Ref.: #66794]	folate polyglutamylation	100	1 of 1
[Ref.: #66794]	suberin monomers biosynthesis	100	2 of 2
[Ref.: #66794]	UDP-GlcNAc biosynthesis	100	3 of 3
[Ref.: #66794]	methylglyoxal degradation	100	5 of 5
[Ref.: #66794]	CDP-diacylglycerol biosynthesis	100	2 of 2
[Ref.: #66794]	phenylacetate degradation (aerobic)	100	5 of 5
[Ref.: #66794]	phenylalanine metabolism	92.31	12 of 13
[Ref.: #66794]	threonine metabolism	90	9 of 10
[Ref.: #66794]	Entner Doudoroff pathway	90	9 of 10
[Ref.: #66794]	d-mannose degradation	88.89	8 of 9
[Ref.: #66794]	molybdenum cofactor biosynthesis	88.89	8 of 9
[Ref.: #66794]	chorismate metabolism	88.89	8 of 9
[Ref.: #66794]	lipid A biosynthesis	88.89	8 of 9
[Ref.: #66794]	C4 and CAM-carbon fixation	87.5	7 of 8
[Ref.: #66794]	photosynthesis	85.71	12 of 14
[Ref.: #66794]	ubiquinone biosynthesis	85.71	6 of 7
[Ref.: #66794]	NAD metabolism	83.33	15 of 18
[Ref.: #66794]	proline metabolism	81.82	9 of 11
[Ref.: #66794]	purine metabolism	80.85	76 of 94
[Ref.: #66794]	3-chlorocatechol degradation	80	4 of 5
[Ref.: #66794]	glycine betaine biosynthesis	80	4 of 5
[Ref.: #66794]	hydrogen production	80	4 of 5
[Ref.: #66794]	alanine metabolism	79.31	23 of 29
[Ref.: #66794]	CO2 fixation in Crenarchaeota	77.78	7 of 9
[Ref.: #66794]	serine metabolism	77.78	7 of 9
[Ref.: #66794]	methionine metabolism	76.92	20 of 26
[Ref.: #66794]	pyrimidine metabolism	75.56	34 of 45
[Ref.: #66794]	acetate fermentation	75	3 of 4
[Ref.: #66794]	glycogen biosynthesis	75	3 of 4
[Ref.: #66794]	CMP-KDO biosynthesis	75	3 of 4
[Ref.: #66794]	butanoate fermentation	75	3 of 4
[Ref.: #66794]	flavin biosynthesis	73.33	11 of 15
[Ref.: #66794]	peptidoglycan biosynthesis	73.33	11 of 15
[Ref.: #66794]	histidine metabolism	72.41	21 of 29
[Ref.: #66794]	heme metabolism	71.43	10 of 14
[Ref.: #66794]	reductive acetyl coenzyme A pathway	71.43	5 of 7
[Ref.: #66794]	glutamate and glutamine metabolism	71.43	20 of 28
[Ref.: #66794]	tyrosine metabolism	71.43	10 of 14
[Ref.: #66794]	propanol degradation	71.43	5 of 7
[Ref.: #66794]	tetrahydrofolate metabolism	71.43	10 of 14
[Ref.: #66794]	tryptophan metabolism	71.05	27 of 38
[Ref.: #66794]	glycolysis	70.59	12 of 17
[Ref.: #66794]	starch degradation	70	7 of 10
[Ref.: #66794]	leucine metabolism	69.23	9 of 13
[Ref.: #66794]	vitamin B1 metabolism	69.23	9 of 13
[Ref.: #66794]	formaldehyde oxidation	66.67	2 of 3
[Ref.: #66794]	arginine metabolism	66.67	16 of 24
[Ref.: #66794]	valine metabolism	66.67	6 of 9
[Ref.: #66794]	L-lactaldehyde degradation	66.67	2 of 3
[Ref.: #66794]	acetoin degradation	66.67	2 of 3
[Ref.: #66794]	glycolate and glyoxylate degradation	66.67	4 of 6
[Ref.: #66794]	isoprenoid biosynthesis	65.38	17 of 26
[Ref.: #66794]	lipid metabolism	64.52	20 of 31
[Ref.: #66794]	lysine metabolism	64.29	27 of 42
[Ref.: #66794]	glutathione metabolism	64.29	9 of 14
[Ref.: #66794]	pentose phosphate pathway	63.64	7 of 11
[Ref.: #66794]	vitamin B6 metabolism	63.64	7 of 11
[Ref.: #66794]	ketogluconate metabolism	62.5	5 of 8
[Ref.: #66794]	non-pathway related	60.53	23 of 38
[Ref.: #66794]	vitamin K metabolism	60	3 of 5
[Ref.: #66794]	lipoate biosynthesis	60	3 of 5
[Ref.: #66794]	metabolism of amino sugars and derivatives	60	3 of 5
[Ref.: #66794]	arachidonate biosynthesis	60	3 of 5
[Ref.: #66794]	glycogen metabolism	60	3 of 5
[Ref.: #66794]	propionate fermentation	60	6 of 10
[Ref.: #66794]	cellulose degradation	60	3 of 5
[Ref.: #66794]	citric acid cycle	57.14	8 of 14
[Ref.: #66794]	cysteine metabolism	55.56	10 of 18
[Ref.: #66794]	urea cycle	53.85	7 of 13
[Ref.: #66794]	oxidative phosphorylation	52.75	48 of 91
[Ref.: #66794]	glycine metabolism	50	5 of 10
[Ref.: #66794]	ethanol fermentation	50	1 of 2
[Ref.: #66794]	4-hydroxyphenylacetate degradation	50	5 of 10
[Ref.: #66794]	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	50	4 of 8
[Ref.: #66794]	aminopropanol phosphate biosynthesis	50	1 of 2
[Ref.: #66794]	mannosylglycerate biosynthesis	50	1 of 2
[Ref.: #66794]	selenocysteine biosynthesis	50	3 of 6
[Ref.: #66794]	isoleucine metabolism	50	4 of 8
[Ref.: #66794]	quinate degradation	50	1 of 2
[Ref.: #66794]	coenzyme M biosynthesis	50	5 of 10
[Ref.: #66794]	polyamine pathway	47.83	11 of 23
[Ref.: #66794]	phenol degradation	45	9 of 20
[Ref.: #66794]	degradation of hexoses	44.44	8 of 18
[Ref.: #66794]	vitamin B12 metabolism	44.12	15 of 34
[Ref.: #66794]	degradation of sugar alcohols	43.75	7 of 16
[Ref.: #66794]	androgen and estrogen metabolism	43.75	7 of 16
[Ref.: #66794]	degradation of aromatic, nitrogen containing compounds	41.67	5 of 12
[Ref.: #66794]	ascorbate metabolism	40.91	9 of 22
[Ref.: #66794]	gallate degradation	40	2 of 5
[Ref.: #66794]	myo-inositol biosynthesis	40	4 of 10
[Ref.: #66794]	factor 420 biosynthesis	40	2 of 5
[Ref.: #66794]	creatinine degradation	40	2 of 5
[Ref.: #66794]	bacilysin biosynthesis	40	2 of 5
[Ref.: #66794]	degradation of pentoses	39.29	11 of 28
[Ref.: #66794]	arachidonic acid metabolism	38.89	7 of 18
[Ref.: #66794]	phenylpropanoid biosynthesis	38.46	5 of 13
[Ref.: #66794]	cholesterol biosynthesis	36.36	4 of 11
[Ref.: #66794]	cyanate degradation	33.33	1 of 3
[Ref.: #66794]	nitrate assimilation	33.33	3 of 9
[Ref.: #66794]	IAA biosynthesis	33.33	1 of 3
[Ref.: #66794]	acetyl CoA biosynthesis	33.33	1 of 3
[Ref.: #66794]	4-hydroxymandelate degradation	33.33	3 of 9
[Ref.: #66794]	allantoin degradation	33.33	3 of 9
[Ref.: #66794]	3-phenylpropionate degradation	33.33	5 of 15
[Ref.: #66794]	(5R)-carbapenem carboxylate biosynthesis	33.33	1 of 3
[Ref.: #66794]	octane oxidation	33.33	1 of 3
[Ref.: #66794]	methanogenesis from CO2	33.33	4 of 12
[Ref.: #66794]	sulfate reduction	30.77	4 of 13
[Ref.: #66794]	phosphatidylethanolamine bioynthesis	30.77	4 of 13
[Ref.: #66794]	aclacinomycin biosynthesis	28.57	2 of 7
[Ref.: #66794]	chlorophyll metabolism	27.78	5 of 18
[Ref.: #66794]	d-xylose degradation	27.27	3 of 11
[Ref.: #66794]	dolichyl-diphosphooligosaccharide biosynthesis	27.27	3 of 11
[Ref.: #66794]	metabolism of disaccharids	27.27	3 of 11
[Ref.: #66794]	carotenoid biosynthesis	27.27	6 of 22
[Ref.: #66794]	vitamin E metabolism	25	1 of 4
[Ref.: #66794]	carnitine metabolism	25	2 of 8
[Ref.: #66794]	lactate fermentation	25	1 of 4
[Ref.: #66794]	catecholamine biosynthesis	25	1 of 4
[Ref.: #66794]	cyclohexanol degradation	25	1 of 4
[Ref.: #66794]	toluene degradation	25	1 of 4
[Ref.: #66794]	dTDPLrhamnose biosynthesis	25	2 of 8
[Ref.: #66794]	degradation of sugar acids	24	6 of 25
[Ref.: #66794]	bile acid biosynthesis, neutral pathway	23.53	4 of 17
		Only first 10 entries are displayed. Click here to see all.

Information on isolation source, the sampling and environmental conditions Isolation, sampling and environmental information

[Ref.: #5395]	Sample type/isolated from	sludge from laboratory reactor
[Ref.: #5395]	Country	Japan
[Ref.: #5395]	Country ISO 3 Code	JPN
[Ref.: #5395]	Continent	Asia

[Ref.: #67770]	Sample type/isolated from	Laboratory-scale anaerobic-aerobic sequential batch reactor operated under EBPR conditions
* marker position based on {}

Isolation sources categories

#Engineered	#Bioreactor	-
#Engineered	#Laboratory	-
#Environmental	#Terrestrial	#Mud (Sludge)

What are isolation sources categories?

[Ref.: #69479]

Global distribution of 16S sequence AB072735 (>99% sequence identity) for Gemmatimonas aurantiaca subclade from Microbeatlas

Aquatic Samples	1743
Soil Samples	654
Animal Samples	127
Plant Samples	82
Total Samples	2606

Information on possible application of the strain and its possible interaction with e.g. potential hosts Safety information

[Ref.: #5395]

Biosafety level

Risk group (German classification)
According to TRBA 466

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #20218]	Gemmatimonas aurantiaca gene for 16S rRNA, partial sequence	AB072735	1446	GenBank ENA	379066 tax ID	*

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Gemmatimonas aurantiaca T-27	GCA_000010305	complete	GenBank ENA	379066 tax ID	*
[Ref.: #66792]	Gemmatimonas aurantiaca T-27	379066.3	complete	PATRIC	379066 tax ID	*

[Ref.: #67770]

GC-content

66 mol%

high performance liquid chromatography (HPLC)

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Gemmatimonas aurantiaca T-27 T-27 NCBI: GCA_000010305.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	93.885	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	88.279	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	66.562	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	76.673	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	94.468	yes
[Ref.: #125438]	motile2+	flagellatedⓘ	yes	52.977	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Gemmatimonas aurantiaca T-27 NCBI: GCA_000010305
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	88.2
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	58.8
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	83.3
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate aerobe	97.9

Availability in culture collections External links

[Ref.: #5395]

Culture collection no.

DSM 14586, JCM 11422, NBRC 100505

[Ref.: #75267]

SI-ID 111713

Literature:

Only first 10 entries are displayed. Click here to see all.

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	N2O Reduction by Gemmatimonas aurantiaca and Potential Involvement of Gemmatimonadetes Bacteria in N2O Reduction in Agricultural Soils.	Oshiki M, Toyama Y, Suenaga T, Terada A, Kasahara Y, Yamaguchi T, Araki N	Microbes Environ	10.1264/jsme2.ME21090	2022	*
Metabolism	Consumption of N2O and other N-cycle intermediates by Gemmatimonas aurantiaca strain T-27.	Chee-Sanford J, Tian D, Sanford R	Microbiology (Reading)	10.1099/mic.0.000847	2019	*
Phylogeny	Roseisolibacter agri gen. nov., sp. nov., a novel slow-growing member of the under-represented phylum Gemmatimonadetes.	Pascual J, Foesel BU, Geppert A, Huber KJ, Boedeker C, Luckner M, Wanner G, Overmann J	Int J Syst Evol Microbiol	10.1099/ijsem.0.002619	2018	*
Metabolism	Nitrous Oxide Reduction by an Obligate Aerobic Bacterium, Gemmatimonas aurantiaca Strain T-27.	Park D, Kim H, Yoon S	Appl Environ Microbiol	10.1128/AEM.00502-17	2017	*
Phylogeny	Longimicrobium terrae gen. nov., sp. nov., an oligotrophic bacterium of the under-represented phylum Gemmatimonadetes isolated through a system of miniaturized diffusion chambers.	Pascual J, Garcia-Lopez M, Bills GF, Genilloud O	Int J Syst Evol Microbiol	10.1099/ijsem.0.000974	2016	*
Phylogeny	Characterization of the microaerophilic, bacteriochlorophyll a-containing bacterium Gemmatimonas phototrophica sp. nov., and emended descriptions of the genus Gemmatimonas and Gemmatimonas aurantiaca.	Zeng Y, Selyanin V, Lukes M, Dean J, Kaftan D, Feng F, Koblizek M	Int J Syst Evol Microbiol	10.1099/ijs.0.000272	2015	*
Cultivation	A hidden pitfall in the preparation of agar media undermines microorganism cultivability.	Tanaka T, Kawasaki K, Daimon S, Kitagawa W, Yamamoto K, Tamaki H, Tanaka M, Nakatsu CH, Kamagata Y	Appl Environ Microbiol	10.1128/AEM.02741-14	2014	*
Metabolism	Carotenoids of Gemmatimonas aurantiaca (Gemmatimonadetes): identification of a novel carotenoid, deoxyoscillol 2-rhamnoside, and proposed biosynthetic pathway of oscillol 2,2'-dirhamnoside.	Takaichi S, Maoka T, Takasaki K, Hanada S	Microbiology (Reading)	10.1099/mic.0.034249-0	2009	*
Phylogeny	Gemmatimonas aurantiaca gen. nov., sp. nov., a gram-negative, aerobic, polyphosphate-accumulating micro-organism, the first cultured representative of the new bacterial phylum Gemmatimonadetes phyl. nov.	Zhang H, Sekiguchi Y, Hanada S, Hugenholtz P, Kim H, Kamagata Y, Nakamura K	Int J Syst Evol Microbiol	10.1099/ijs.0.02520-0	2003	*

References

#5395

Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 14586

#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) .

#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 .

#75267

Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.: StrainInfo: A central database for resolving microbial strain identifiers . ( DOI 10.60712/SI-ID111713.1 )

#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 .

* These data were automatically processed and therefore are not curated

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