Cite this strain

"Stenotrophomonas panacihumi" (CCUG 59872, KCTC 22893, JCM 16536)

BacDive ID 155979

Type strain

Culture col. no. CCUG 59872 KCTC 22893 JCM 16536

NCBI tax ID(s) 676599

Special Collections CCUG JCM KCTC

History M. K. Kim MK06.

Links

"Stenotrophomonas panacihumi" CCUG 59872 is an aerobe bacterium that was isolated from Soil of a ginseng field.

aerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 409912

KCTC 22893,JCM 16536,CCUG 59872

@ref 20215

Last LPSN update 2026-02-09 11:37:44

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Lysobacterales

Family Lysobacteraceae

Genus Stenotrophomonas

Species "Stenotrophomonas panacihumi"

Full scientific name Stenotrophomonas panacihumi Yi et al. 2010

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	98.75

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
62146	positive	growth	30
67770	positive	growth	30

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
62146	aerobe
125439	obligate aerobe	99.2

Spore formation

@ref	Spore formation	Confidence
125439		92.1
125438		91.422

Observation

67770

Observationquinones: Q-8

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	starch degradation	100	10 of 10
66794	ppGpp biosynthesis	100	4 of 4
66794	formaldehyde oxidation	100	3 of 3
66794	ribulose monophosphate pathway	100	2 of 2
66794	acetate fermentation	100	4 of 4
66794	methylglyoxal degradation	100	5 of 5
66794	valine metabolism	100	9 of 9
66794	adipate degradation	100	2 of 2
66794	pentose phosphate pathway	100	11 of 11
66794	Entner Doudoroff pathway	100	10 of 10
66794	biotin biosynthesis	100	4 of 4
66794	quinate degradation	100	2 of 2
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	coenzyme A metabolism	100	4 of 4
66794	sulfopterin metabolism	100	4 of 4
66794	ceramide biosynthesis	100	1 of 1
66794	UDP-GlcNAc biosynthesis	100	3 of 3
66794	folate polyglutamylation	100	1 of 1
66794	tetrahydrofolate metabolism	100	14 of 14
66794	suberin monomers biosynthesis	100	2 of 2
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	palmitate biosynthesis	100	22 of 22
66794	threonine metabolism	90	9 of 10
66794	aspartate and asparagine metabolism	88.89	8 of 9
66794	chorismate metabolism	88.89	8 of 9
66794	molybdenum cofactor biosynthesis	88.89	8 of 9
66794	lipid A biosynthesis	88.89	8 of 9
66794	CO2 fixation in Crenarchaeota	88.89	8 of 9
66794	isoleucine metabolism	87.5	7 of 8
66794	photosynthesis	85.71	12 of 14
66794	leucine metabolism	84.62	11 of 13
66794	glycolate and glyoxylate degradation	83.33	5 of 6
66794	NAD metabolism	83.33	15 of 18
66794	purine metabolism	82.98	78 of 94
66794	glutamate and glutamine metabolism	82.14	23 of 28
66794	cellulose degradation	80	4 of 5
66794	glycogen metabolism	80	4 of 5
66794	gallate degradation	80	4 of 5
66794	heme metabolism	78.57	11 of 14
66794	citric acid cycle	78.57	11 of 14
66794	serine metabolism	77.78	7 of 9
66794	phenylalanine metabolism	76.92	10 of 13
66794	alanine metabolism	75.86	22 of 29
66794	butanoate fermentation	75	3 of 4
66794	glycogen biosynthesis	75	3 of 4
66794	ketogluconate metabolism	75	6 of 8
66794	C4 and CAM-carbon fixation	75	6 of 8
66794	dTDPLrhamnose biosynthesis	75	6 of 8
66794	CMP-KDO biosynthesis	75	3 of 4
66794	gluconeogenesis	75	6 of 8
66794	peptidoglycan biosynthesis	73.33	11 of 15
66794	vitamin B6 metabolism	72.73	8 of 11
66794	histidine metabolism	72.41	21 of 29
66794	ubiquinone biosynthesis	71.43	5 of 7
66794	propanol degradation	71.43	5 of 7
66794	tyrosine metabolism	71.43	10 of 14
66794	cardiolipin biosynthesis	71.43	5 of 7
66794	tryptophan metabolism	71.05	27 of 38
66794	propionate fermentation	70	7 of 10
66794	methionine metabolism	69.23	18 of 26
66794	vitamin B1 metabolism	69.23	9 of 13
66794	pyrimidine metabolism	68.89	31 of 45
66794	degradation of sugar acids	68	17 of 25
66794	d-mannose degradation	66.67	6 of 9
66794	L-lactaldehyde degradation	66.67	2 of 3
66794	cysteine metabolism	66.67	12 of 18
66794	IAA biosynthesis	66.67	2 of 3
66794	flavin biosynthesis	66.67	10 of 15
66794	octane oxidation	66.67	2 of 3
66794	acetoin degradation	66.67	2 of 3
66794	cyanate degradation	66.67	2 of 3
66794	glycolysis	64.71	11 of 17
66794	lipid metabolism	64.52	20 of 31
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	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	62.5	5 of 8
66794	lipoate biosynthesis	60	3 of 5
66794	metabolism of amino sugars and derivatives	60	3 of 5
66794	vitamin K metabolism	60	3 of 5
66794	arginine metabolism	58.33	14 of 24
66794	non-pathway related	57.89	22 of 38
66794	glutathione metabolism	57.14	8 of 14
66794	lysine metabolism	57.14	24 of 42
66794	metabolism of disaccharids	54.55	6 of 11
66794	isoprenoid biosynthesis	53.85	14 of 26
66794	degradation of pentoses	53.57	15 of 28
66794	phenol degradation	50	10 of 20
66794	mannosylglycerate biosynthesis	50	1 of 2
66794	dolichol and dolichyl phosphate biosynthesis	50	1 of 2
66794	ethanol fermentation	50	1 of 2
66794	aminopropanol phosphate biosynthesis	50	1 of 2
66794	pantothenate biosynthesis	50	3 of 6
66794	myo-inositol biosynthesis	50	5 of 10
66794	polyamine pathway	47.83	11 of 23
66794	3-phenylpropionate degradation	46.67	7 of 15
66794	urea cycle	46.15	6 of 13
66794	arachidonic acid metabolism	44.44	8 of 18
66794	reductive acetyl coenzyme A pathway	42.86	3 of 7
66794	oxidative phosphorylation	41.76	38 of 91
66794	degradation of aromatic, nitrogen containing compounds	41.67	5 of 12
66794	vitamin B12 metabolism	41.18	14 of 34
66794	hydrogen production	40	2 of 5
66794	arachidonate biosynthesis	40	2 of 5
66794	D-cycloserine biosynthesis	40	2 of 5
66794	3-chlorocatechol degradation	40	2 of 5
66794	glycine metabolism	40	4 of 10
66794	degradation of hexoses	38.89	7 of 18
66794	sulfate reduction	38.46	5 of 13
66794	phosphatidylethanolamine bioynthesis	38.46	5 of 13
66794	carnitine metabolism	37.5	3 of 8
66794	androgen and estrogen metabolism	37.5	6 of 16
66794	acetyl CoA biosynthesis	33.33	1 of 3
66794	4-hydroxymandelate degradation	33.33	3 of 9
66794	enterobactin biosynthesis	33.33	1 of 3
66794	sulfoquinovose degradation	33.33	1 of 3
66794	(5R)-carbapenem carboxylate biosynthesis	33.33	1 of 3
66794	sphingosine metabolism	33.33	2 of 6
66794	ascorbate metabolism	31.82	7 of 22
66794	phenylpropanoid biosynthesis	30.77	4 of 13
66794	coenzyme M biosynthesis	30	3 of 10
66794	dolichyl-diphosphooligosaccharide biosynthesis	27.27	3 of 11
66794	carotenoid biosynthesis	27.27	6 of 22
66794	catecholamine biosynthesis	25	1 of 4
66794	lactate fermentation	25	1 of 4
66794	toluene degradation	25	1 of 4
66794	cyclohexanol degradation	25	1 of 4
66794	alginate biosynthesis	25	1 of 4
66794	nitrate assimilation	22.22	2 of 9
66794	daunorubicin biosynthesis	22.22	2 of 9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Agriculture	#Field
#Environmental	#Terrestrial	#Soil
#Host	#Plants	#Herbaceous plants (Grass,Crops)

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
62146	Soil of a ginseng field	Republic of Korea	KOR	Asia
67770	Soil of a ginseng field in Daejeon	Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence GQ856217 (>99% sequence identity) for Stenotrophomonas from Microbeatlas

Aquatic Samples	169
Soil Samples	588
Animal Samples	387
Plant Samples	350
Total Samples	1494

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM143164v1 assembly for Stenotrophomonas panacihumi JCM 16536	contig	GCA_001431645	676599.3	2744054482	676599	45.57

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Stenotrophomonas panacihumi strain MK06 16S ribosomal RNA gene, partial sequence	GQ856217	1476		676599

GC content

@ref	GC-content (mol%)	Method
67770	67.8	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: Stenotrophomonas panacihumi JCM 16536
NCBI: GCA_001431645

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	92.10	no
125439	motility	BacteriaNetⓘ	yes	68.60	no
125439	gram_stain	BacteriaNetⓘ	negative	83.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.20	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Stenotrophomonas panacihumi strain JCM 16536
PATRIC: 676599.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.75	no
125438	anaerobic	anaerobicⓘ	no	97.84	yes
125438	spore-forming	spore-formingⓘ	no	91.42	no
125438	aerobic	aerobicⓘ	yes	82.79	no
125438	thermophilic	thermophileⓘ	no	97.96	no
125438	flagellated	motile2+ⓘ	yes	70.05	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Enzymology	Stenotrophomonas panacihumi sp. nov., isolated from soil of a ginseng field.	Yi H, Srinivasan S, Kim MK.	J Microbiol	10.1007/s12275-010-0006-0	2010
	Stenotrophomonas goyi sp. nov., a novel bacterium associated with the alga Chlamydomonas reinhardtii.	Torres MJ, Fakhimi N, Dubini A, Gonzalez-Ballester D.	F1000Res	10.12688/f1000research.134978.3	2023
Phylogeny	Stenotrophomonas bentonitica sp. nov., isolated from bentonite formations.	Sanchez-Castro I, Ruiz-Fresneda MA, Bakkali M, Kampfer P, Glaeser SP, Busse HJ, Lopez-Fernandez M, Martinez-Rodriguez P, Merroun ML.	Int J Syst Evol Microbiol	10.1099/ijsem.0.002016	2017

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 )
#62146	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 59872
#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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"Stenotrophomonas panacihumi" (CCUG 59872, KCTC 22893, JCM 16536)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Pathway annotation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_001431645

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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