Paracoccus chinensis (CGMCC 1.7655, NBRC 104937, KS-11)

Name: Paracoccus chinensis (CGMCC 1.7655, NBRC 104937, KS-11)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 133454

Type strain

Strain Designation KS-11

Culture col. no. CGMCC 1.7655 NBRC 104937 KS-11

NCBI tax ID(s) 525640

Special Collections Literature strains

Links

Paracoccus chinensis KS-11 is an aerobe, Gram-negative, motile bacterium that was isolated from freshwater sediment .

Gram-negative motile ovoid-shaped aerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 393891

NBRC 104937

@ref 20215

Last LPSN update 2026-02-09 11:31:57

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodobacterales

Family Paracoccaceae

Genus Paracoccus

Species Paracoccus chinensis

Full scientific name Paracoccus chinensis Li et al. 2009

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility	Confidence
29041	negative	1.75 µm	1.15 µm	ovoid-shaped
125438	negative					96.462

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
29041	positive	growth	25-40
29041	positive	optimum	28-37

Culture pH

@ref	Ability	Type	PH	PH range
29041	positive	optimum	7.5-9
29041	positive	growth	6-9.5	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
29041	aerobe
125439	obligate aerobe	98.4

Spore formation

29041

Spore formationno

Halophily

@ref	Salt	Growth	Tested relation	Concentration
29041	NaCl	positive	growth	0-1 %

Observation

29041

Observationaggregates in chains

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29041	30089 ChEBI	acetate	+	carbon source
29041	16449 ChEBI	alanine	+	carbon source
29041	22653 ChEBI	asparagine	+	carbon source
29041	16947 ChEBI	citrate	+	carbon source
29041	28757 ChEBI	fructose	+	carbon source
29041	33984 ChEBI	fucose	+	carbon source
29041	28260 ChEBI	galactose	+	carbon source
29041	24265 ChEBI	gluconate	+	carbon source
29041	17234 ChEBI	glucose	+	carbon source
29041	27570 ChEBI	histidine	+	carbon source
29041	17716 ChEBI	lactose	+	carbon source
29041	17306 ChEBI	maltose	+	carbon source
29041	37684 ChEBI	mannose	+	carbon source
29041	28053 ChEBI	melibiose	+	carbon source
29041	17632 ChEBI	nitrate	+	reduction
29041	26271 ChEBI	proline	+	carbon source
29041	15361 ChEBI	pyruvate	+	carbon source
29041	33942 ChEBI	ribose	+	carbon source
29041	17814 ChEBI	salicin	+	carbon source
29041	30911 ChEBI	sorbitol	+	carbon source
29041	17992 ChEBI	sucrose	+	carbon source
29041	27082 ChEBI	trehalose	+	carbon source
29041	18222 ChEBI	xylose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
29041	catalase	+	1.11.1.6
29041	cytochrome oxidase	+	1.9.3.1
29041	urease	+	3.5.1.5

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	valine metabolism	100	9 of 9
66794	allantoin degradation	100	9 of 9
66794	adipate degradation	100	2 of 2
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	phenylmercury acetate degradation	100	2 of 2
66794	ethanol fermentation	100	2 of 2
66794	octane oxidation	100	3 of 3
66794	acetate fermentation	100	4 of 4
66794	palmitate biosynthesis	100	22 of 22
66794	ppGpp biosynthesis	100	4 of 4
66794	methylglyoxal degradation	100	5 of 5
66794	butanoate fermentation	100	4 of 4
66794	resorcinol degradation	100	2 of 2
66794	C4 and CAM-carbon fixation	100	8 of 8
66794	denitrification	100	2 of 2
66794	UDP-GlcNAc biosynthesis	100	3 of 3
66794	suberin monomers biosynthesis	100	2 of 2
66794	coenzyme A metabolism	100	4 of 4
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	gluconeogenesis	100	8 of 8
66794	formaldehyde oxidation	100	3 of 3
66794	folate polyglutamylation	100	1 of 1
66794	photosynthesis	92.86	13 of 14
66794	vitamin B1 metabolism	92.31	12 of 13
66794	Entner Doudoroff pathway	90	9 of 10
66794	chorismate metabolism	88.89	8 of 9
66794	propanol degradation	85.71	6 of 7
66794	ubiquinone biosynthesis	85.71	6 of 7
66794	reductive acetyl coenzyme A pathway	85.71	6 of 7
66794	glutathione metabolism	85.71	12 of 14
66794	phenylalanine metabolism	84.62	11 of 13
66794	leucine metabolism	84.62	11 of 13
66794	proline metabolism	81.82	9 of 11
66794	threonine metabolism	80	8 of 10
66794	3-chlorocatechol degradation	80	4 of 5
66794	ethylmalonyl-CoA pathway	80	4 of 5
66794	peptidoglycan biosynthesis	80	12 of 15
66794	citric acid cycle	78.57	11 of 14
66794	serine metabolism	77.78	7 of 9
66794	CO2 fixation in Crenarchaeota	77.78	7 of 9
66794	molybdenum cofactor biosynthesis	77.78	7 of 9
66794	lipid A biosynthesis	77.78	7 of 9
66794	aspartate and asparagine metabolism	77.78	7 of 9
66794	isoleucine metabolism	75	6 of 8
66794	sulfopterin metabolism	75	3 of 4
66794	glutamate and glutamine metabolism	75	21 of 28
66794	toluene degradation	75	3 of 4
66794	ketogluconate metabolism	75	6 of 8
66794	CMP-KDO biosynthesis	75	3 of 4
66794	glycogen biosynthesis	75	3 of 4
66794	oxidative phosphorylation	72.53	66 of 91
66794	alanine metabolism	72.41	21 of 29
66794	NAD metabolism	72.22	13 of 18
66794	cysteine metabolism	72.22	13 of 18
66794	cardiolipin biosynthesis	71.43	5 of 7
66794	lysine metabolism	71.43	30 of 42
66794	heme metabolism	71.43	10 of 14
66794	purine metabolism	71.28	67 of 94
66794	propionate fermentation	70	7 of 10
66794	urea cycle	69.23	9 of 13
66794	degradation of sugar alcohols	68.75	11 of 16
66794	pyrimidine metabolism	66.67	30 of 45
66794	acetyl CoA biosynthesis	66.67	2 of 3
66794	cyanate degradation	66.67	2 of 3
66794	L-lactaldehyde degradation	66.67	2 of 3
66794	enterobactin biosynthesis	66.67	2 of 3
66794	d-mannose degradation	66.67	6 of 9
66794	flavin biosynthesis	66.67	10 of 15
66794	sulfoquinovose degradation	66.67	2 of 3
66794	methane metabolism	66.67	2 of 3
66794	selenocysteine biosynthesis	66.67	4 of 6
66794	acetoin degradation	66.67	2 of 3
66794	histidine metabolism	65.52	19 of 29
66794	glycolysis	64.71	11 of 17
66794	tetrahydrofolate metabolism	64.29	9 of 14
66794	pentose phosphate pathway	63.64	7 of 11
66794	vitamin B6 metabolism	63.64	7 of 11
66794	non-pathway related	63.16	24 of 38
66794	dTDPLrhamnose biosynthesis	62.5	5 of 8
66794	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	62.5	5 of 8
66794	polyamine pathway	60.87	14 of 23
66794	tryptophan metabolism	60.53	23 of 38
66794	3-phenylpropionate degradation	60	9 of 15
66794	phenylacetate degradation (aerobic)	60	3 of 5
66794	hydrogen production	60	3 of 5
66794	degradation of sugar acids	60	15 of 25
66794	vitamin K metabolism	60	3 of 5
66794	lipoate biosynthesis	60	3 of 5
66794	arginine metabolism	58.33	14 of 24
66794	lipid metabolism	58.06	18 of 31
66794	isoprenoid biosynthesis	57.69	15 of 26
66794	methionine metabolism	57.69	15 of 26
66794	nitrate assimilation	55.56	5 of 9
66794	sulfate reduction	53.85	7 of 13
66794	degradation of pentoses	53.57	15 of 28
66794	glycolate and glyoxylate degradation	50	3 of 6
66794	coenzyme M biosynthesis	50	5 of 10
66794	myo-inositol biosynthesis	50	5 of 10
66794	cyclohexanol degradation	50	2 of 4
66794	glycine metabolism	50	5 of 10
66794	kanosamine biosynthesis II	50	1 of 2
66794	quinate degradation	50	1 of 2
66794	arachidonic acid metabolism	50	9 of 18
66794	biotin biosynthesis	50	2 of 4
66794	tyrosine metabolism	50	7 of 14
66794	degradation of aromatic, nitrogen containing compounds	50	6 of 12
66794	phenol degradation	50	10 of 20
66794	metabolism of disaccharids	45.45	5 of 11
66794	ascorbate metabolism	45.45	10 of 22
66794	4-hydroxymandelate degradation	44.44	4 of 9
66794	androgen and estrogen metabolism	43.75	7 of 16
66794	benzoyl-CoA degradation	42.86	3 of 7
66794	glycine betaine biosynthesis	40	2 of 5
66794	arachidonate biosynthesis	40	2 of 5
66794	gallate degradation	40	2 of 5
66794	carnitine metabolism	37.5	3 of 8
66794	degradation of hexoses	33.33	6 of 18
66794	IAA biosynthesis	33.33	1 of 3
66794	sphingosine metabolism	33.33	2 of 6
66794	(5R)-carbapenem carboxylate biosynthesis	33.33	1 of 3
66794	carotenoid biosynthesis	31.82	7 of 22
66794	phenylpropanoid biosynthesis	30.77	4 of 13
66794	starch degradation	30	3 of 10
66794	d-xylose degradation	27.27	3 of 11
66794	dolichyl-diphosphooligosaccharide biosynthesis	27.27	3 of 11
66794	lactate fermentation	25	1 of 4
66794	methanogenesis from CO2	25	3 of 12
66794	alginate biosynthesis	25	1 of 4
66794	bile acid biosynthesis, neutral pathway	23.53	4 of 17
66794	phosphatidylethanolamine bioynthesis	23.08	3 of 13
66794	vitamin B12 metabolism	20.59	7 of 34

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Freshwater
#Environmental	#Aquatic	#Lake (large)
#Environmental	#Aquatic	#Pond (small)
#Environmental	#Aquatic	#River (Creek)

Isolation

29041

Sample typefreshwater sediment (river, lake, pond)

Taxonmaps

69479

Global distribution of 16S sequence EU660389 (>99% sequence identity) for Paracoccus from Microbeatlas

Aquatic Samples	4482
Soil Samples	1527
Animal Samples	9541
Plant Samples	890
Total Samples	16440

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	IMG-taxon 2617270907 annotated assembly for Paracoccus chinensis CGMCC 1.7655	contig	GCA_900102885	525640.3	2617270907	525640	60.08

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
29041	Paracoccus chinensis strain KS-11 16S ribosomal RNA gene, partial sequence	EU660389	1362		525640
124043	Paracoccus chinensis gene for 16S rRNA, partial sequence, strain: NBRC 104937.	AB682230	1388		525640

GC content

29041

GC-content (mol%)69

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Paracoccus chinensis CGMCC 1.7655
NCBI: GCA_900102885

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	89.30	no
125439	motility	BacteriaNetⓘ	no	51.50	no
125439	gram_stain	BacteriaNetⓘ	negative	89.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Paracoccus chinensis strain CGMCC 1.7655
PATRIC: 525640.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.46	no
125438	anaerobic	anaerobicⓘ	no	92.50	yes
125438	spore-forming	spore-formingⓘ	no	85.87	yes
125438	aerobic	aerobicⓘ	yes	81.00	no
125438	thermophilic	thermophileⓘ	no	94.82	no
125438	flagellated	motile2+ⓘ	no	59.76	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Paracoccus chinensis sp. nov., isolated from sediment of a reservoir.	Li HF, Qu JH, Yang JS, Li ZJ, Yuan HL	Int J Syst Evol Microbiol	10.1099/ijs.0.004705-0	2009
Phylogeny	Paracoccus salipaludis sp. nov., isolated from saline-alkaline soil.	Dong X, Zhang G, Xiong Q, Liu D, Wang D, Liu Y, Wu G, Li P, Luo Y, Zhang R	Int J Syst Evol Microbiol	10.1099/ijsem.0.003065	2018
Phylogeny	Paracoccus niistensis sp. nov., isolated from forest soil, India.	Dastager SG, Deepa CK, Li WJ, Tang SK, Pandey A	Antonie Van Leeuwenhoek	10.1007/s10482-010-9515-4	2010

References

#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 )
#25471	IJSEM 2670 2009 ( DOI 10.1099/ijs.0.004705-0 , PubMed 19625438 )
#29041	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 #25471
#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 )
#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 .
#124043	Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy .
#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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Paracoccus chinensis (CGMCC 1.7655, NBRC 104937, KS-11)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Observation

Metabolite utilization

Enzymes

Pathway annotation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_900102885

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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