Mycoplasmoides fastidiosum (DSM 21204, ATCC 33229, NCTC 10180)

Name: Mycoplasmoides fastidiosum (DSM 21204, ATCC 33229, NCTC 10180)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 8601

Type strain

Strain Designation 4822

Culture col. no. DSM 21204 ATCC 33229 NCTC 10180 4822

NCBI tax ID(s) 92758

Special Collections DSMZ

History DSMZ <- J. K. Davis {2007} <- J. G. Tully <- NCTC; NCTC 10180 <- MRLN <- R. M. Lemcke and J. Poland

Links

Mycoplasmoides fastidiosum 4822 is a bacterium of the family Mycoplasmoidaceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 40403

ATCC 33229,NCTC 10180,DSM 21204

@ref 20215

Last LPSN update 2026-05-29 08:26:54

Domain Bacteria

Phylum Mycoplasmatota

Class Mollicutes

Order Mycoplasmoidales

Family Mycoplasmoidaceae

Genus Mycoplasmoides

Species Mycoplasmoides fastidiosum

Full scientific name Mycoplasmoides fastidiosum (Lemcke and Poland 1980) Gupta et al. 2018

Synonyms (1)

Mycoplasma fastidiosum

Morphology

Cell morphology

@ref	Motility	Confidence
125438		94.33

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
15479	SP4-Z MEDIUM (DSMZ Medium 1076b)		Medium recipe at MediaDive	Name: SP4-Z MEDIUM (DSMZ Medium 1076b) Composition: Agar 10.0 g/l Tryptone 10.0 g/l Bacto peptone 5.0 g/l PPLO broth 3.5 g/l Urea 2.0 g/l Glucose 1.0 g/l None 1.0 g/l Yeast extract 0.4 g/l DNA 0.2 g/l CMRL 1066 Fetal bovine serum Swine serum Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
15479	positive	growth	37

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125438		93.845

Isolation, sampling and environmental information

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
15479	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	ASM2449827v1 assembly for Mycoplasmoides fastidiosum 4822	complete	GCA_024498275	92758.3	8048113802	92758	94.75
124043	ASM3081524v1 assembly for Mycoplasmoides fastidiosum DSM 21204	contig	GCA_030815245	92758.4	2931415050	92758	73.36

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Mycoplasma fastidiosum 16S ribosomal RNA gene, partial sequence; 16S-23S ribosomal RNA intergenic spacer, complete sequence; and 23S ribosomal RNA gene, partial sequence	AY781782	986		92758
15479	Mycoplasma fastidiosum 16S ribosomal RNA, partial sequence	AF125878	1455		92758

Genome-based predictions

Predictions

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Mycoplasmoides fastidiosum 4822
NCBI: GCA_024498275.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	59.98	no
125438	anaerobic	anaerobicⓘ	no	82.28	no
125438	aerobic	aerobicⓘ	no	88.65	no
125438	spore-forming	spore-formingⓘ	no	93.85	no
125438	thermophilic	thermophileⓘ	no	96.59	yes
125438	flagellated	motile2+ⓘ	no	94.33	no

Literature

References

#15479	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 21204
#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 )
#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) .
#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 )
#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