Cupriavidus lacunae (KACC 19624, JCM 32674)

Name: Cupriavidus lacunae (KACC 19624, JCM 32674)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 164321

Type strain

Culture col. no. KACC 19624 JCM 32674

NCBI tax ID(s) 2666307

Special Collections JCM

Links

Cupriavidus lacunae JCM 32674 is a mesophilic prokaryote that was isolated from Pond-side soil of an artificial pond in Seoul.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 272404

LMG 31391,JCM 32674

@ref 20215

Last LPSN update 2025-12-16 10:09:26

Domain Pseudomonadati

Phylum Pseudomonadota

Class Betaproteobacteria

Order Burkholderiales

Family Burkholderiaceae

Genus Cupriavidus

Species Cupriavidus lacunae

Full scientific name Cupriavidus lacunae Feng et al. 2019

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	97
125438	negative	97

Culture and growth conditions

Culture temp

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

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125438	aerobe	90.244
125439	obligate aerobe	97.3

Spore formation

@ref	Spore formation	Confidence
125439		97.1

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
67770	Pond-side soil of an artificial pond in Seoul	Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence MG745916 (>99% sequence identity) for Cupriavidus from Microbeatlas

Aquatic Samples	5686
Soil Samples	15055
Animal Samples	7044
Plant Samples	2775
Total Samples	30560

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM335305v1 assembly for Cupriavidus lacunae S23	contig	GCA_003353055	2219262.3	2899826043	2666307	26.97

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Cupriavidus lacunae 16S ribosomal RNA gene, partial sequence	MG745916	1468		2666307

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Cupriavidus lacunae S23
NCBI: GCA_003353055

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	97.10	no
125439	motility	BacteriaNetⓘ	yes	73.00	no
125439	gram_stain	BacteriaNetⓘ	negative	97.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	97.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Cupriavidus lacunae S23
NCBI: GCA_003353055.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.00	no
125438	anaerobic	anaerobicⓘ	no	97.30	no
125438	aerobic	aerobicⓘ	yes	90.24	no
125438	spore-forming	spore-formingⓘ	no	88.16	no
125438	thermophilic	thermophileⓘ	no	97.99	no
125438	flagellated	motile2+ⓘ	yes	85.67	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Factors Influencing the Colostrum's Microbiota: A Systematic Review of the Literature.	Tzani A, Xixi NA, Sokou R, Karapati E, Iliodromiti Z, Volaki P, Paliatsiou S, Iacovidou N, Boutsikou T.	Children (Basel)	10.3390/children12101336	2025
Phylogeny	Contrasting Diversity and Composition of Human Colostrum Microbiota in a Maternal Cohort With Different Ethnic Origins but Shared Physical Geography (Island Scale).	Xie W, Zhang H, Ni Y, Peng Y.	Front Microbiol	10.3389/fmicb.2022.934232	2022
	Disinfection Performance of a Drinking Water Bottle System With a UV Subtype C LED Cap Against Waterborne Pathogens and Heterotrophic Contaminants.	Mariita RM, Blumenstein SA, Beckert CM, Gombas T, Randive RV.	Front Microbiol	10.3389/fmicb.2021.719578	2021
Phylogeny	Cupriavidus lacunae sp. nov., isolated from pond-side soil.	Feng T, Kim KH, Oh J, Jeon CO	Antonie Van Leeuwenhoek	10.1007/s10482-018-1187-5	2018

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 )
#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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Cupriavidus lacunae (KACC 19624, JCM 32674)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Isolation

Taxonmaps

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_003353055

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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