Acetobacterium paludosum (DSM 8237, Z-4092, Z-B12)

Name: Acetobacterium paludosum (DSM 8237, Z-4092, Z-B12)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 5407

Type strain

Strain Designation Z-4092, Z-B12

Culture col. no. DSM 8237 Z-4092 Z-B12

NCBI tax ID(s) 52693

Special Collections DSMZ

History DSMZ <- A. N. Nozhevnikova, Russian Academy of Sciences, Inst. of Microbiology, Moscow, Russia; Z-B12

Links

Acetobacterium paludosum Z-4092 is an anaerobe bacterium that was isolated from bog sediment.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 45320

ATCC 51793,DSM 8237

@ref 20215

Last LPSN update 2026-05-29 08:26:10

Domain Bacteria

Phylum Bacillota

Class Clostridia

Order Eubacteriales

Family Eubacteriaceae

Genus Acetobacterium

Species Acetobacterium paludosum

Full scientific name Acetobacterium paludosum Kotsyurbenko et al. 1997

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
3214	ACETOBACTERIUM FIMETARIUM MEDIUM (DSMZ Medium 614)		Medium recipe at MediaDive	Name: ACETOBACTERIUM FIMETARIUM MEDIUM (DSMZ Medium 614) Composition: D-Fructose 9.8912 g/l NaHCO3 0.98912 g/l Na2S x 9 H2O 0.692384 g/l MgCl2 x 6 H2O 0.514342 g/l Yeast extract 0.49456 g/l NH4Cl 0.326409 g/l KH2PO4 0.326409 g/l KCl 0.326409 g/l MgSO4 x 7 H2O 0.0296736 g/l Nitrilotriacetic acid 0.0148368 g/l NaCl 0.0098912 g/l MnSO4 x H2O 0.0049456 g/l ZnSO4 x 7 H2O 0.00178042 g/l CoSO4 x 7 H2O 0.00178042 g/l CaCl2 x 2 H2O 0.00098912 g/l FeSO4 x 7 H2O 0.00098912 g/l Sodium resazurin 0.00049456 g/l NiCl2 x 6 H2O 0.000296736 g/l AlK(SO4)2 x 12 H2O 0.000197824 g/l CuSO4 x 5 H2O 9.8912e-05 g/l H3BO3 9.8912e-05 g/l Na2MoO4 x 2 H2O 9.8912e-05 g/l Pyridoxine hydrochloride 9.8912e-05 g/l (DL)-alpha-Lipoic acid 4.9456e-05 g/l p-Aminobenzoic acid 4.9456e-05 g/l Calcium D-(+)-pantothenate 4.9456e-05 g/l Thiamine HCl 4.9456e-05 g/l Riboflavin 4.9456e-05 g/l Nicotinic acid 4.9456e-05 g/l Folic acid 1.97824e-05 g/l Biotin 1.97824e-05 g/l Na2WO4 x 2 H2O 3.95648e-06 g/l Na2SeO3 x 5 H2O 2.96736e-06 g/l Vitamin B12 9.8912e-07 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
3214	positive	growth	20

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
3214	anaerobe
125439	facultative anaerobe	94.837

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Wetland (Swamp)
#Environmental	#Terrestrial	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
3214	bog sediment	near Moscow	Russia	RUS	Europe

Taxonmaps

69479

Global distribution of 16S sequence X96958 (>99% sequence identity) for Acetobacterium paludosum from Microbeatlas

Aquatic Samples	49
Soil Samples	7
Animal Samples	7
Plant Samples	0
Total Samples	63

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
3214	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
66792	ASM808659v1 assembly for Acetobacterium paludosum DSM 8237	contig	GCA_008086595	52693.4	2995188184	52693	66.55
124043	ASM1428445v1 assembly for Acetobacterium paludosum DSM 8237	contig	GCA_014284455	52693.5	8065737351	52693	64.55

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
3214	A.paludosum 16S rRNA gene	X96958	1459		52693

GC content

3214

GC-content (mol%)41.7

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM808659v1 assembly for Acetobacterium paludosum DSM 8237
BV-BRC: 52693.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	94.84	no
125439	gram_stain	BacteriaNetⓘ	variable	67.56	no
125439	motility	BacteriaNetⓘ	yes	77.05	no
125439	spore_formation	BacteriaNetⓘ	yes	78.58	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acetobacterium paludosum strain DSM 8237
PATRIC: 52693.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	60.38	no
125438	anaerobic	anaerobicⓘ	yes	86.90	yes
125438	spore-forming	spore-formingⓘ	yes	53.03	no
125438	aerobic	aerobicⓘ	no	91.99	no
125438	thermophilic	thermophileⓘ	no	84.83	yes
125438	flagellated	motile2+ⓘ	yes	65.28	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Using gas mixtures of CO, CO₂ and H₂ as microbial substrates: the do's and don'ts of successful technology transfer from laboratory to production scale.	Takors R, Kopf M, Mampel J, Bluemke W, Blombach B, Eikmanns B, Bengelsdorf FR, Weuster-Botz D, Durre P.	Microb Biotechnol	10.1111/1751-7915.13270	2018
Phylogeny	Diversity and abundance of bacteria in an underground oil-storage cavity.	Watanabe K, Kodama Y, Kaku N.	BMC Microbiol	10.1186/1471-2180-2-23	2002
Genetics	Insights into the genome structure of four acetogenic bacteria with specific reference to the Wood-Ljungdahl pathway.	Esposito A, Tamburini S, Triboli L, Ambrosino L, Chiusano ML, Jousson O	Microbiologyopen	10.1002/mbo3.938	2019

References

#3214	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 8237
#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) .
#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 )

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Acetobacterium paludosum (DSM 8237, Z-4092, Z-B12)

Name and taxonomic classification

Morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_008086595

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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