Cite this strain

"Profundibacterium mesophilum" (JCM 17872, NRRL B-59665, KAUST100406-0324)

BacDive ID 133848

Type strain

Strain Designation KAUST100406-0324

Culture col. no. JCM 17872 NRRL B-59665 KAUST100406-0324

NCBI tax ID(s) 1258573

Special Collections JCM Literature strains

History P. Y. Lai KAUST100406-0324.

Links

"Profundibacterium mesophilum" KAUST100406-0324 is an aerobe, Gram-negative, motile bacterium that was isolated from marine sediment.

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

Name and taxonomic classification

SI-ID 131859

JCM 17872

@ref 20215

Last LPSN update 2026-02-09 11:32:03

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodobacterales

Family Roseobacteraceae

Genus "Profundibacterium"

Species "Profundibacterium mesophilum"

Full scientific name Profundibacterium mesophilum corrig. Lai 2012

Synonyms (2)

"Profundibacterium mesophila"
Profundibacterium mesophilum

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Flagellum arrangement	Confidence
30668	negative	0.2 µm	0.2 µm	coccus-shaped	gliding
125439	negative					97.5
125438	negative					97.5

Pigmentation

30668

Productionno

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
30668	positive	growth	15-25
30668	positive	optimum	22.5
67770	positive	growth	25

Culture pH

@ref	Ability	Type	PH	PH range
30668	positive	optimum	7.5
30668	positive	growth	6-8.5	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
30668	aerobe
125439	aerobe	90.6

Spore formation

@ref	Spore formation	Confidence
30668
125439		94.7

Halophily

@ref	Salt	Growth	Tested relation	Concentration
30668	NaCl	positive	optimum	4 %

Observation

67770

Observationquinones: Q-10

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30668	29016 ChEBI	arginine	+	carbon source
30668	16947 ChEBI	citrate	+	carbon source
30668	28260 ChEBI	galactose	+	carbon source
30668	17234 ChEBI	glucose	+	carbon source
30668	29987 ChEBI	glutamate	+	carbon source
30668	17754 ChEBI	glycerol	+	carbon source
30668	17272 ChEBI	propionate	+	carbon source
30668	15361 ChEBI	pyruvate	+	carbon source
30668	30031 ChEBI	succinate	+	carbon source
30668	18222 ChEBI	xylose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
30668	acid phosphatase	+	3.1.3.2
30668	alkaline phosphatase	+	3.1.3.1
30668	catalase	+	1.11.1.6
30668	cytochrome oxidase	+	1.9.3.1

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Marine
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
30668	marine sediment
67770	Sea-floor sediment collected from the Red Sea	Saudi Arabia	SAU	Asia

Taxonmaps

69479

Global distribution of 16S sequence JF776971 (>99% sequence identity) for Profundibacterium mesophilum subclade from Microbeatlas

Aquatic Samples	284
Soil Samples	56
Animal Samples	10
Plant Samples	1
Total Samples	351

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM983514v1 assembly for Profundibacterium mesophilum KAUST100406-0324	contig	GCA_009835145	1037889.3	2989027852	1037889	51.54

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
30668	Profundibacterium mesophilum KAUST100406-0324 16S ribosomal RNA gene, partial sequence	JF776971	1436		1037889

GC content

@ref	GC-content (mol%)	Method
30668	64
67770	64	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: Profundibacterium mesophilum KAUST100406-0324
NCBI: GCA_009835145

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	94.70	no
125439	motility	BacteriaNetⓘ	yes	53.90	no
125439	gram_stain	BacteriaNetⓘ	negative	97.50	no
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	90.60	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Profundibacterium mesophilum KAUST100406-0324
PATRIC: 1037889.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.50	yes
125438	anaerobic	anaerobicⓘ	no	96.51	yes
125438	spore-forming	spore-formingⓘ	no	84.84	no
125438	aerobic	aerobicⓘ	yes	82.64	yes
125438	thermophilic	thermophileⓘ	no	94.36	yes
125438	flagellated	motile2+ⓘ	yes	54.24	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Profundibacterium mesophilum gen. nov., sp. nov., a novel member in the family Rhodobacteraceae isolated from deep-sea sediment in the Red Sea, Saudi Arabia.	Lai PY, Miao L, Lee OO, Liu LL, Zhou XJ, Xu Y, Al-Suwailem A, Qian PY	Int J Syst Evol Microbiol	10.1099/ijs.0.041525-0	2012

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 )
#26999	IJSEM 1007 2013 ( DOI 10.1099/ijs.0.041525-0 , PubMed 22685108 )
#30668	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 #26999
#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) .
#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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"Profundibacterium mesophilum" (JCM 17872, NRRL B-59665, KAUST100406-0324)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Observation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_009835145

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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