Mycobacterium smegmatis (DSM 43059, CCM 1889, IMRU N 525)

Name: Mycobacterium smegmatis (DSM 43059, CCM 1889, IMRU N 525)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 8309

Type strain

Culture col. no. DSM 43059 CCM 1889 IMRU N 525 NCTC 525 BUCSAV 267

NCBI tax ID(s) 1772

Special Collections DSMZ

History <- M. Kocur, CCM; CCM 1889 <- BICZAS; BUCSAV 267 <- VSCHT; (Mycobacterium phlei)

Links

Mycobacterium smegmatis DSM 43059 is a human pathogen that was isolated from hay.

human pathogen genome sequence Bacteria

Name and taxonomic classification

SI-ID 50132

DSM 43059,CCM 1889,NCTC 525

@ref 20215

Last LPSN update 2026-02-09 11:19:40

Domain Bacteria

Phylum Actinomycetota

Class Actinomycetes

Order Mycobacteriales

Family Mycobacteriaceae

Genus Mycobacterium

Species Mycobacterium smegmatis

Full scientific name Mycobacterium smegmatis (Trevisan 1889) Lehmann and Neumann 1899 (Approved Lists 1980)

Synonyms (2)

"Bacillus smegmatis"
Mycolicibacterium smegmatis

BacDive ID	Other strains from **Mycobacterium smegmatis** (27)	Type strain
8324	M. smegmatis GA 735, Cornell 3, 14 133 0010, DSM 43756, ... (type strain)
8256	M. smegmatis Minetti 41, DSM 43479, SN 220
8310	M. smegmatis DSM 43061, CCM 2228, NCTC 523
8311	M. smegmatis 52-L-1, DSM 43078, IMRU 672
8312	M. smegmatis DSM 43079, IMRU 853
8313	M. smegmatis DSM 43080, IMRU 580
8314	M. smegmatis 52-Q-1, DSM 43081, IMRU 595
8315	M. smegmatis DSM 43277, ATCC 101, ATCC 14468, NCIB 9953, ...
8316	M. smegmatis 74C, Schering 250, DSM 43286, ATCC 356, NCTC ...
8317	M. smegmatis 169, Schering 275, DSM 43299, ATCC 114, CCUG ...
8318	M. smegmatis DSM 43464, ATCC 27204, IMET 10646, SN 2
8319	M. smegmatis 607, DSM 43465, ATCC 607, NRRL B-692, SN ...
8320	M. smegmatis DSM 43466, IMRU 7992, SN 35
8321	M. smegmatis DSM 43467, IMRU 9822, SN 44
8322	M. smegmatis 114, DSM 43468, ATCC 27205, IMET 10647, IMRU ...
8323	M. smegmatis 7449, DSM 43469, IMET 10648, SN 46
8325	M. smegmatis DSM 44200, HKI 0056 (SG 987)
8326	M. smegmatis 987-M10, DSM 44201, HKI 0062
104243	M. smegmatis ST033288(HKI),
136747	M. smegmatis CIP 56.5, CCTM 170, NCTC 53
139485	M. smegmatis F21, CIP 73.26
143909	M. smegmatis CCUG 21821, ATCC 19979, NCTC 333
143910	M. smegmatis CCUG 21822, ATCC 23028
143911	M. smegmatis CCUG 21823, ATCC 23032
143912	M. smegmatis CCUG 21824, ATCC 23037
145167	M. smegmatis CCUG 27786
145276	M. smegmatis CCUG 28062, NCTC 3821

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	positive	95.3

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
10723	MIDDLEBROOK MEDIUM (DSMZ Medium 645)		Medium recipe at MediaDive	Name: MIDDLEBROOK MEDIUM (DSMZ Medium 645) Composition: Bacto Middlebrook 7H10 agar 20.9945 g/l Glycerol Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
10723	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Plants	#Herbaceous plants (Grass,Crops)
#Host Body Product	#Plant	#Straw

Isolation

10723

Sample typehay

Interaction and safety

Risk assessment

@ref	Pathogenicity human	Pathogenicity animal	Biosafety level	Biosafety level comment
10723			2	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	NCBI tax ID	Score
66792	Mycolicibacterium smegmatis strain FDAARGOS_1484	complete		1772.65	1772	99.11
66792	Mycolicibacterium smegmatis strain FDAARGOS_1484	complete		1772.70	1772	99.11
66792	Mycolicibacterium smegmatis strain FDAARGOS_1484	complete		1772.63	1772	99.11
124043	ASM1993074v1 assembly for Mycolicibacterium smegmatis FDAARGOS_1484	complete	GCA_019930745	1772.52	1772	97.47

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Mycolicibacterium smegmatis strain FDAARGOS_1484
PATRIC: 1772.52

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	60.00	no
125439	motility	BacteriaNetⓘ	no	73.80	no
125439	gram_stain	BacteriaNetⓘ	positive	95.30	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.00	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Mycolicibacterium smegmatis strain FDAARGOS_1484 strain Not applicable
PATRIC: 1772.52

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	88.68	no
125438	anaerobic	anaerobicⓘ	no	94.70	no
125438	spore-forming	spore-formingⓘ	no	60.99	no
125438	aerobic	aerobicⓘ	yes	89.62	no
125438	thermophilic	thermophileⓘ	no	97.00	no
125438	flagellated	motile2+ⓘ	no	87.37	no

Literature

Title	Authors	Journal	DOI	Year
Rapidly growing, acid fast bacteria. I. Species' descriptions of Mycobacterium phlei Lehmann and Neumann and Mycobacterium smegmatis (Trevisan) Lehmann and Neumann.	GORDON RE, SMITH MM.	J Bacteriol	10.1128/jb.66.1.41-48.1953	1953

References

#10723	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 43059
#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) .
#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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Mycobacterium smegmatis (DSM 43059, CCM 1889, IMRU N 525)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_019930745

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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