Cite this strain

Cellulosimicrobium funkei (MCC 2761, LMG 28646, KCTC 39527)

Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 164257

Type strain

Culture col. no. MCC 2761 LMG 28646 KCTC 39527 JCM 32432

NCBI tax ID(s) 1612203

Special Collections JCM KCTC

Links

Name and taxonomic classification

Cellulosimicrobium funkei JCM 32432 is a mesophilic bacterium that was isolated from Panagal freshwater reservoir at Nalgonda.

mesophilic genome sequence 16S sequence Bacteria

Culture Collection Numbers

MCC 2761 LMG 28646 KCTC 39527 JCM 32432

Last LPSN update 2024-12-09 10:52:45

Domain Bacteria

Phylum Actinomycetota

Class Actinomycetes

Order Micrococcales

Family Promicromonosporaceae

Genus Cellulosimicrobium

Species Cellulosimicrobium funkei

Full scientific name Cellulosimicrobium funkei Brown et al. 2006

Synonyms (1)

Cellulosimicrobium aquatile

BacDive ID	Other strains from **Cellulosimicrobium funkei** (9)	Type strain
12581	C. funkei C8821, W6122, DSM 16025, ATCC BAA-886, CCUG ... (type strain)
135381	C. funkei 896.84, CIP 101288
135494	C. funkei 881.84, CIP 101208
137362	C. funkei 833.84, CIP 101186
139140	C. funkei 843.84, CIP 101185
139141	C. funkei R520, CIP 102520
139165	C. funkei R682, CIP 102521
139167	C. funkei R860, CIP 102522
155770	C. funkei CCUG 59260

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	positive	100

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
67770	positive	growth	28	mesophilic

Physiology and metabolism

Observation

67770

Observationquinones: MK-9(H₄)

Spore formation

@ref	Spore formation	Confidence
125439		93

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.5

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
67770	Panagal freshwater reservoir at Nalgonda	Telangana	India	IND	Asia

Taxonmaps

69479

Global distribution of 16S sequence LN812019 (>99% sequence identity) for Cellulosimicrobium from Microbeatlas

Aquatic Samples	417
Soil Samples	1873
Animal Samples	1680
Plant Samples	1505
Total Samples	5475

Safety information

Sequence information

GC content

@ref	GC-content (mol%)	Method
67770	73.8	high performance liquid chromatography (HPLC)

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Cellulosimicrobium aquatile partial 16S rRNA gene,type strain 3bpT	LN812019	1436		1612203

Genome sequences

@ref	Description	Accession	Assembly level	Database	NCBI tax ID
67770	Cellulosimicrobium aquatile 3bp	GCA_900155925	contig		1612203

Genome-based predictions

Predictions

Predictions from Diaspora project based on:
Cellulosimicrobium aquatile 3bp
NCBI: GCA_900155925

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	93.00	no
125439	motility	BacteriaNetⓘ	no	86.60	no
125439	gram_stain	BacteriaNetⓘ	positive	100.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.50	no

Predictions from Diaspora project based on:
Cellulosimicrobium aquatile 3bp
NCBI: GCA_900155925.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	86.37	no
125438	anaerobic	anaerobicⓘ	no	91.20	no
125438	aerobic	aerobicⓘ	yes	76.06	no
125438	spore-forming	spore-formingⓘ	no	55.16	no
125438	thermophilic	thermophileⓘ	no	97.50	no
125438	flagellated	motile2+ⓘ	no	67.50	no

External links

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Cellulosimicrobium aquatile sp. nov., isolated from Panagal reservoir, Nalgonda, India.	Sultanpuram VR, Mothe T, Chintalapati S, Chintalapati VR	Antonie Van Leeuwenhoek	10.1007/s10482-015-0588-y	2015

Culture collection no.

Culture collection no.
MCC 2761
LMG 28646
KCTC 39527
JCM 32432

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 .

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