Sulfitobacter delicatus (DSM 16477, ATCC BAA-321, KCTC 32183)

Name: Sulfitobacter delicatus (DSM 16477, ATCC BAA-321, KCTC 32183)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 13796

Type strain

Culture col. no. DSM 16477 ATCC BAA-321 KCTC 32183 KMM 3584 LMG 20554

NCBI tax ID(s) 218672

Special Collections DSMZ KCTC Literature strains

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Sulfitobacter delicatus DSM 16477 is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from starfish Stellaster equestris.

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

Name and taxonomic classification

SI-ID 84769

DSM 16477,KCTC 32183,LMG 20554,ATCC BAA-321

@ref 20215

Last LPSN update 2025-12-16 09:50:11

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodobacterales

Family Roseobacteraceae

Genus Sulfitobacter

Species Sulfitobacter delicatus

Full scientific name Sulfitobacter delicatus Ivanova et al. 2004

BacDive ID	Other strains from **Sulfitobacter delicatus** (1)	Type strain
175132	S. delicatus 4RW5_S5, DSM 110034

Morphology

Cell morphology

@ref	Gram stain	Cell width	Cell shape	Motility
29915	negative	0.8 µm	rod-shaped
125438	negative

Pigmentation

29915

Productionno

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
6465	BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514)		Medium recipe at MediaDive	Name: BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514) Composition: NaCl 19.45 g/l MgCl2 5.9 g/l Bacto peptone 5.0 g/l Na2SO4 3.24 g/l CaCl2 1.8 g/l Yeast extract 1.0 g/l KCl 0.55 g/l NaHCO3 0.16 g/l Fe(III) citrate 0.1 g/l KBr 0.08 g/l SrCl2 0.034 g/l H3BO3 0.022 g/l Na2HPO4 0.008 g/l Na-silicate 0.004 g/l NaF 0.0024 g/l (NH4)NO3 0.0016 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
6465	positive	growth	25	mesophilic
29915	positive	growth	12-37
29915	positive	optimum	25	mesophilic

Culture pH

@ref	Ability	Type	PH
29915	positive	optimum	7.75
29915	positive	growth	6.0-10.0

Physiology and metabolism

Oxygen tolerance

29915

Oxygen toleranceaerobe

Spore formation

@ref	Spore formation
29915
125439

Halophily

@ref	Salt	Growth	Tested relation	Concentration
29915	NaCl	positive	growth	01-08 %
29915	NaCl	positive	optimum	3.5 %

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29915	18240 ChEBI	4-hydroxy-L-proline	+	carbon source
29915	33984 ChEBI	fucose	+	carbon source
29915	28260 ChEBI	galactose	+	carbon source
29915	5291 ChEBI	gelatin	+	carbon source
29915	17716 ChEBI	lactose	+	carbon source
29915	29864 ChEBI	mannitol	+	carbon source
29915	17632 ChEBI	nitrate	+	reduction

Enzymes

@ref	Value	Activity	Ec
29915	catalase	+	1.11.1.6
29915	cytochrome oxidase	+	1.9.3.1

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Invertebrates (Other)	#Echinodermata

Isolation

@ref	Sample type	Host species	Geographic location	Continent
6465	starfish Stellaster equestris	Stellaster equestris	South China Sea	Asia

Taxonmaps

69479

Global distribution of 16S sequence DQ915634 (>99% sequence identity) for Sulfitobacter from Microbeatlas

Aquatic Samples	2251
Soil Samples	83
Animal Samples	278
Plant Samples	12
Total Samples	2624

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
6465	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 2634166359 annotated assembly for Sulfitobacter delicatus DSM 16477	scaffold	GCA_900102535	218672.5	2634166359	218672	66.1

Genome browser: GCA_900102535

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Sulfitobacter delicatus strain LMG20554 16S ribosomal RNA gene, partial sequence	DQ915634	1307		218672
6465	Sulfitobacter delicatus strain KMM 3584T 16S ribosomal RNA gene, partial sequence	AY180103	1429		218672

GC content

6465

GC-content (mol%)60.3

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Sulfitobacter delicatus DSM 16477
NCBI: GCA_900102535

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	91.70	no
125439	motility	BacteriaNetⓘ	no	50.00	no
125439	gram_stain	BacteriaNetⓘ	negative	84.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	89.10	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Sulfitobacter delicatus DSM 16477
NCBI: GCA_900102535.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.50	yes
125438	anaerobic	anaerobicⓘ	no	96.16	no
125438	aerobic	aerobicⓘ	yes	84.57	no
125438	spore-forming	spore-formingⓘ	no	88.41	yes
125438	thermophilic	thermophileⓘ	no	96.79	yes
125438	flagellated	motile2+ⓘ	yes	57.03	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Sulfitobacter maritimus sp. nov., isolated from coastal sediment.	Lian FB, Li YQ, Zhang J, Jiang S, Du ZJ	Int J Syst Evol Microbiol	10.1099/ijsem.0.004659	2021
Phylogeny	Sulfitobacter delicatus sp. nov. and Sulfitobacter dubius sp. nov., respectively from a starfish (Stellaster equestris) and sea grass (Zostera marina).	Ivanova EP, Gorshkova NM, Sawabe T, Zhukova NV, Hayashi K, Kurilenko VV, Alexeeva Y, Buljan V, Nicolau DV, Mikhailov VV, Christen R	Int J Syst Evol Microbiol	10.1099/ijs.0.02654-0	2004

References

#6465	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 16477
#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 )
#29915	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 #26286 (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 )
#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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Sulfitobacter delicatus (DSM 16477, ATCC BAA-321, KCTC 32183)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

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_900102535

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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