Acetobacter xylinus (JCM 7664, IFO 13693, NBRC 13693)

Name: Acetobacter xylinus (JCM 7664, IFO 13693, NBRC 13693)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 165557

Type strain

Culture col. no. JCM 7664 IFO 13693 NBRC 13693

NCBI tax ID(s) 1234668 28448

Special Collections JCM

Links

Acetobacter xylinus JCM 7664 is a mesophilic prokaryote of the family Acetobacteraceae.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 62426

JCM 7664,IFO 13693,NBRC 13693

@ref 20215

Last LPSN update 2025-12-16 10:09:42

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Acetobacteraceae

Genus Acetobacter

Species Acetobacter xylinus

Full scientific name Acetobacter xylinus corrig. (Brown 1886) Yamada 1984

Synonyms (8)

Acetobacter xylinum
Acetobacter aceti subsp. xylinus
"Komagatabacter xylinus"
"Bacterium xylinum"
Acetobacter aceti subsp. xylinum
Gluconoacetobacter xylinus
Gluconacetobacter xylinus
Komagataeibacter xylinus

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	97.333
125439	negative	98.5

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
125439	aerobe	91.3

Spore formation

@ref	Spore formation	Confidence
125438		92.077
125439		98.3

Isolation, sampling and environmental information

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	NCBI tax ID	Score
66792	ASM96450v1 assembly for Komagataeibacter xylinus NBRC 13693	contig	GCA_000964505	1234668.3	1234668	37.33

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
124043	Acetobacter xylinum 16S rRNA, partial sequence.	AB003986	155		28448
124043	Gluconacetobacter xylinus gene for 16S rRNA, partial sequence, strain: NBRC 13693.	AB680481	1414		1234668

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Komagataeibacter xylinus NBRC 13693
NCBI: GCA_000964505

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	91.30	no
125439	gram_stain	BacteriaNetⓘ	negative	98.50	no
125439	motility	BacteriaNetⓘ	yes	79.60	no
125439	spore_formation	BacteriaNetⓘ	no	98.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Komagataeibacter xylinus NBRC 13693
PATRIC: 1234668.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.33	no
125438	anaerobic	anaerobicⓘ	no	88.58	no
125438	spore-forming	spore-formingⓘ	no	92.08	no
125438	aerobic	aerobicⓘ	yes	77.78	no
125438	thermophilic	thermophileⓘ	no	97.67	yes
125438	flagellated	motile2+ⓘ	no	56.67	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Acetan and Acetan-Like Polysaccharides: Genetics, Biosynthesis, Structure, and Viscoelasticity.	Trcek J, Dogsa I, Accetto T, Stopar D.	Polymers (Basel)	10.3390/polym13050815	2021
Genetics	Insights into Bacterial Cellulose Biosynthesis from Different Carbon Sources and the Associated Biochemical Transformation Pathways in Komagataeibacter sp. W1.	Wang SS, Han YH, Chen JL, Zhang DC, Shi XX, Ye YX, Chen DL, Li M.	Polymers (Basel)	10.3390/polym10090963	2018
	Optimization of Citrus Pulp Waste-Based Medium for Improved Bacterial Nanocellulose Production.	Minardi C, Bersanetti D, Sarlin E, Santala V, Mangayil R.	Microorganisms	10.3390/microorganisms12102095	2024
	Sustainable production of bacterial nanocellulose from date fruit waste using Bacillus haynesii for waste valorisation and crystal violet dye removal.	Makharita RR, El-Ahmady El-Naggar N, Baghdadi AM, Hamouda RA.	Sci Rep	10.1038/s41598-025-04711-z	2025
Genetics	Characterization of the Putative Acylated Cellulose Synthase Operon in Komagataeibacter xylinus E25.	Szymczak I, Pietrzyk-Brzezinska AJ, Duszynski K, Ryngajllo M.	Int J Mol Sci	10.3390/ijms23147851	2022
	Distribution and genome structures of temperate phages in acetic acid bacteria.	Omata K, Hibi N, Nakano S, Komoto S, Sato K, Nunokawa K, Amano S, Ueda K, Takano H.	Sci Rep	10.1038/s41598-021-00998-w	2021
	The Effect of Adding Modified Chitosan on the Strength Properties of Bacterial Cellulose for Clinical Applications.	Lipovka A, Kharchenko A, Dubovoy A, Filipenko M, Stupak V, Mayorov A, Fomenko V, Geydt P, Parshin D.	Polymers (Basel)	10.3390/polym13121995	2021
	Hydrogel Fiber Cultivation Method for Forming Bacterial Cellulose Microspheres.	Higashi K, Miki N.	Micromachines (Basel)	10.3390/mi9010036	2018
Metabolism	Comparison of productivity and quality of bacterial nanocellulose synthesized using culture media based on seven sugars from biomass.	Chen G, Wu G, Chen L, Wang W, Hong FF, Jonsson LJ.	Microb Biotechnol	10.1111/1751-7915.13401	2019
Biotechnology	Use of Industrial Wastes as Sustainable Nutrient Sources for Bacterial Cellulose (BC) Production: Mechanism, Advances, and Future Perspectives.	Kadier A, Ilyas RA, Huzaifah MRM, Harihastuti N, Sapuan SM, Harussani MM, Azlin MNM, Yuliasni R, Ibrahim R, Atikah MSN, Wang J, Chandrasekhar K, Islam MA, Sharma S, Punia S, Rajasekar A, Asyraf MRM, Ishak MR.	Polymers (Basel)	10.3390/polym13193365	2021
Phylogeny	Komagataeibacter intermedius V-05: An Acetic Acid Bacterium Isolated from Vinegar Industry, with High Capacity for Bacterial Cellulose Production in Soybean Molasses Medium.	Gomes RJ, de Sousa Faria-Tischer PC, Tischer CA, Constantino LV, de Freitas Rosa M, Chideroli RT, de Padua Pereira U, Spinosa WA, Spinosa WA.	Food Technol Biotechnol	10.17113/ftb.59.04.21.7148	2021
	Utilization of various fruit juices as carbon source for production of bacterial cellulose by Acetobacter xylinum NBRC 13693	Kurosumi Akihiro, Sasaki Chizuru, Yamashita Yuya, Nakamura Yoshitoshi.	Carbohydrate polymers.	10.1016/j.carbpol.2008.11.009	2009
	Bacterial Nanocellulose Hydrogel: A Promising Alternative Material for the Fabrication of Engineered Vascular Grafts.	Liu D, Meng Q, Hu J.	Polymers (Basel)	10.3390/polym15183812	2023
	Bacterial Cellulose Properties Fulfilling Requirements for a Biomaterial of Choice in Reconstructive Surgery and Wound Healing.	Jankau J, Blazynska-Spychalska A, Kubiak K, Jedrzejczak-Krzepkowska M, Pankiewicz T, Ludwicka K, Dettlaff A, Peksa R.	Front Bioeng Biotechnol	10.3389/fbioe.2021.805053	2021
	From Residues to Added-Value Bacterial Biopolymers as Nanomaterials for Biomedical Applications.	Blanco FG, Hernandez N, Rivero-Buceta V, Maestro B, Sanz JM, Mato A, Hernandez-Arriaga AM, Prieto MA.	Nanomaterials (Basel)	10.3390/nano11061492	2021
	Minerals consumption by Acetobacter xylinum on cultivation medium on coconut water.	Almeida DM, Prestes RA, da Fonseca AF, Woiciechowski AL, Wosiacki G.	Braz J Microbiol	10.1590/s1517-83822013005000012	2013
Metabolism	Genome sequence and plasmid transformation of the model high-yield bacterial cellulose producer Gluconacetobacter hansenii ATCC 53582.	Florea M, Reeve B, Abbott J, Freemont PS, Ellis T.	Sci Rep	10.1038/srep23635	2016
Metabolism	Vitamin C enhances bacterial cellulose production in Gluconacetobacter xylinus.	Keshk SM	Carbohydr Polym	10.1016/j.carbpol.2013.08.060	2013
Enzymology	Cloning of cellulose synthase genes from Acetobacter xylinum JCM 7664: implication of a novel set of cellulose synthase genes.	Umeda Y, Hirano A, Ishibashi M, Akiyama H, Onizuka T, Ikeuchi M, Inoue Y	DNA Res	10.1093/dnares/6.2.109	1999

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 )
#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;
#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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Acetobacter xylinus (JCM 7664, IFO 13693, NBRC 13693)

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

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_000964505

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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