Acetomicrobium flavidum (DSM 20664, ATCC 43122, P)

Name: Acetomicrobium flavidum (DSM 20664, ATCC 43122, P)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 1580

Type strain

Strain Designation P

Culture col. no. DSM 20664 ATCC 43122 P

NCBI tax ID(s) 49896

Special Collections DSMZ

History DSMZ <- E. Soutschek, P

Links

Acetomicrobium flavidum P is an anaerobe bacterium that was isolated from sewage sludge.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 72

LMG 6941,DSM 20664,ATCC 43122

@ref 20215

Last LPSN update 2026-05-29 08:25:17

Domain Bacteria

Phylum Synergistota

Class Synergistia

Order Synergistales

Family Synergistaceae

Genus Acetomicrobium

Species Acetomicrobium flavidum

Full scientific name Acetomicrobium flavidum Soutschek et al. 1985

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
8988	PYG MEDIUM (MODIFIED) (DSMZ Medium 104)		Medium recipe at MediaDive	Name: PYG MEDIUM (modified) (DSMZ Medium 104) Composition: Yeast extract 10.0 g/l Peptone 5.0 g/l Trypticase peptone 5.0 g/l Beef extract 5.0 g/l Glucose 5.0 g/l L-Cysteine HCl x H2O 0.5 g/l NaHCO3 0.4 g/l NaCl 0.08 g/l K2HPO4 0.04 g/l KH2PO4 0.04 g/l MgSO4 x 7 H2O 0.02 g/l CaCl2 x 2 H2O 0.01 g/l Hemin 0.005 g/l Ethanol 0.0038 g/l Resazurin 0.001 g/l Tween 80 Vitamin K1 NaOH Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
8988	positive	growth	55

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
8988	anaerobe
125438	anaerobe	91.459
125439	anaerobe	97.619

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Waste	#Sewage sludge

Isolation

8988

Sample typesewage sludge

Taxonmaps

69479

Global distribution of 16S sequence FR733692 (>99% sequence identity) for Acetomicrobium from Microbeatlas

Aquatic Samples	1383
Soil Samples	172
Animal Samples	498
Plant Samples	81
Total Samples	2134

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
8988	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
124043	IMG-taxon 2695420966 annotated assembly for Acetomicrobium flavidum DSM 20664	contig	GCA_900129645	49896.3	2695420966	49896	77.66

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Acetomicrobium flavidum partial 16S rRNA gene, type strain DSM20664T	FR733692	1516		49896

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: IMG-taxon 2695420966 annotated assembly for Acetomicrobium flavidum DSM 20664
BV-BRC: 49896.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	97.62	no
125439	gram_stain	BacteriaNetⓘ	variable	47.84	no
125439	motility	BacteriaNetⓘ	yes	49.56	no
125439	spore_formation	BacteriaNetⓘ	yes	59.49	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acetomicrobium flavidum strain DSM 20664
PATRIC: 49896.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	83.29	no
125438	anaerobic	anaerobicⓘ	yes	91.46	yes
125438	spore-forming	spore-formingⓘ	no	80.41	no
125438	aerobic	aerobicⓘ	no	97.53	yes
125438	thermophilic	thermophileⓘ	yes	75.97	yes
125438	flagellated	motile2+ⓘ	yes	63.20	no

Literature

Title	Authors	Journal	DOI	Year
Identification of a Putative CodY Regulon in the Gram-Negative Phylum Synergistetes.	Geng J, Luo S, Shieh HR, Wang HY, Hu S, Chen YM.	Int J Mol Sci	10.3390/ijms23147911	2022

References

#8988	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 20664
#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 )
#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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Acetomicrobium flavidum (DSM 20664, ATCC 43122, P)

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_900129645

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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