Acetobacter indonesiensis (DSM 15552, IFO 16471, JCM 10948)

Name: Acetobacter indonesiensis (DSM 15552, IFO 16471, JCM 10948)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 18

Type strain

Strain Designation 5H-1

Culture col. no. DSM 15552 IFO 16471 JCM 10948 NBRC 16471 NRIC 0313 3 more

NCBI tax ID(s) 1307908 104101

Special Collections DSMZ JCM

Links

Acetobacter indonesiensis 5H-1 is a mesophilic prokaryote that was isolated from rotten zirzak .

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 14619

LMG 19824,IFO 16471,JCM 10948,NBRC 16471,DSM 15552,BCRC 17167,CCRC 17167

@ref 20215

Last LPSN update 2025-12-16 09:46:45

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Acetobacteraceae

Genus Acetobacter

Species Acetobacter indonesiensis

Full scientific name Acetobacter indonesiensis Lisdiyanti et al. 2001

BacDive ID	Other strains from **Acetobacter indonesiensis** (1)	Type strain
23488	A. indonesiensis DSM 3805, NCIB 7215, ATCC 9432, CECT 474, ...

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	93.333
125439	negative	97.9

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
5998	ACETIC ACID BACTERIUM MEDIUM (DSMZ Medium 989)		Medium recipe at MediaDive	Name: ACETIC ACID BACTERIUM MEDIUM (DSMZ Medium 989) Composition: Agar 15.0 g/l Glucose 5.0 g/l Yeast extract 5.0 g/l Bacto peptone 5.0 g/l MgSO4 x 7 H2O 1.0 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
5998	positive	growth	30	mesophilic
67770	positive	growth	30	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	aerobe	93.1

Spore formation

@ref	Spore formation	Confidence
125438		91.21
125439		98

Observation

67770

Observationquinones: Q-9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Plants	#Decomposing plant
#Host Body-Site	#Plant	#Fruit (Seed)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
5998	rotten zirzak (fruit)		Indonesia	IDN	Asia
67770	Zirzak	Yogyakarta	Indonesia	IDN	Asia

Taxonmaps

69479

Global distribution of 16S sequence AB681067 (>99% sequence identity) for Acetobacter from Microbeatlas

Aquatic Samples	55
Soil Samples	39
Animal Samples	77
Plant Samples	62
Total Samples	233

Interaction and safety

Risk assessment

@ref	Pathogenicity human	Biosafety level	Biosafety level comment
5998	yes, in single cases	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM799107v1 assembly for Acetobacter indonesiensis NBRC 16471	contig	GCA_007991075	104101.10		104101	60.81
124043	ASM2599571v1 assembly for Acetobacter indonesiensis NRIC 0313	contig	GCA_025995715	1307908.3	8117366814	1307908	60.2
67770	ASM96394v1 assembly for Acetobacter indonesiensis 5H-1	scaffold	GCA_000963945	1231340.3	2899141447	104101	40.93

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Acetobacter indonesiensis gene for 16S rRNA, partial sequence, strain: NBRC 16471	AB681067	1415		104101
5998	Acetobacter indonesiensis gene for 16S rRNA, partial sequence, strain: NRIC 0313	AB032356	1454		104101

GC content

@ref	GC-content (mol%)	Method
67770	53.7	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: Acetobacter indonesiensis 5H-1
NCBI: GCA_000963945

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	93.10	no
125439	gram_stain	BacteriaNetⓘ	negative	97.90	no
125439	motility	BacteriaNetⓘ	yes	79.20	no
125439	spore_formation	BacteriaNetⓘ	no	98.00	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acetobacter indonesiensis 5H-1
PATRIC: 1231340.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	93.33	no
125438	anaerobic	anaerobicⓘ	no	90.27	no
125438	spore-forming	spore-formingⓘ	no	91.21	no
125438	aerobic	aerobicⓘ	yes	83.70	no
125438	thermophilic	thermophileⓘ	no	97.97	yes
125438	flagellated	motile2+ⓘ	no	52.88	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Modeling Drosophila gut microbe interactions reveals metabolic interconnectivity.	Schonborn JW, Stewart FA, Enriquez KM, Akhtar I, Droste A, Waschina S, Beller M.	iScience	10.1016/j.isci.2021.103216	2021
Metabolism	A squalene-hopene cyclase in Schizosaccharomyces japonicus represents a eukaryotic adaptation to sterol-limited anaerobic environments.	Bouwknegt J, Wiersma SJ, Ortiz-Merino RA, Doornenbal ESR, Buitenhuis P, Giera M, Muller C, Pronk JT.	Proc Natl Acad Sci U S A	10.1073/pnas.2105225118	2021

References

#5998	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 15552
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#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 .
#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 )

Rate our new design

Content

About us

Acetobacter indonesiensis (DSM 15552, IFO 16471, JCM 10948)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_007991075

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

Help

Social Media