Name: Mycobacterium saopaulense CCUG 66554
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 156903

Type strain:

Species: Mycobacterium saopaulense

Culture col. no.: CCUG 66554, LMG 28586, JCM 32436, INCQS 0733

Strain history: LMG 28586 <-- S. C. Leão; Univ. Fed. de São Paulo, Brazil; EPM 10906.

NCBI tax ID(s): 1578165 (species)

SI-ID 396484

LMG 28586, CCUG 66554

Special Collections

CCUG
JCM

Section

When using BacDive for research please cite our paper

Mycobacterium saopaulense CCUG 66554 is a microaerophile, mesophilic bacterium that was isolated from Human eye,corneal scraping.

mesophilic
microaerophile
16S sequence
Bacteria
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Bacteria*
	Phylum	*Actinomycetota*
	Class	*Actinomycetes*
	Order	*Mycobacteriales*
	Family	*Mycobacteriaceae*
	Genus	*Mycobacterium*
	Species	**Mycobacterium saopaulense**
	Full Scientific Name (LPSN)	Mycobacterium saopaulense Nogueira et al. 2015

Synonym

Mycobacteroides saopaulense

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Motility	no Confidence in %90.5

[Ref.: #63351]

Incubation period

2 days

Information on culture and growth conditions Culture and growth conditions

	Temperatures	Kind of temperature		Temperature
[Ref.: #63351]			growth	37 °C
[Ref.: #67770]			growth	37 °C

Information on physiology and metabolism Physiology and metabolism

[Ref.: #63351]	Oxygen tolerance	microaerophile
[Ref.: #125439]	Oxygen tolerance	obligate aerobe Confidence in %91.5

Information on isolation source, the sampling and environmental conditions Isolation, sampling and environmental information

[Ref.: #63351]	Sample type/isolated from	Human eye,corneal scraping
[Ref.: #63351]	Sampling date	2000-09-11
[Ref.: #63351]	Geographic location (country and/or sea, region)	Sao Paulo
[Ref.: #63351]	Country	Brazil
[Ref.: #63351]	Country ISO 3 Code	BRA
[Ref.: #63351]	Continent	Middle and South America

[Ref.: #67770]	Sample type/isolated from	Corneal of a patient with infectious crystalline keratopathy
[Ref.: #67770]	Geographic location (country and/or sea, region)	São Paulo
[Ref.: #67770]	Country	Brazil
[Ref.: #67770]	Country ISO 3 Code	BRA
[Ref.: #67770]	Continent	Middle and South America
* marker position based on {}

Isolation sources categories

#Host	#Human	-
#Host Body-Site	#Organ	#Eye
#Infection	#Patient	-

What are isolation sources categories?

[Ref.: #69479]

Global distribution of 16S sequence KM973037 (>99% sequence identity) for Mycobacteroides from Microbeatlas

Aquatic Samples	6199
Soil Samples	7046
Animal Samples	7007
Plant Samples	1742
Total Samples	21994

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #67770]	Mycobacteroides saopaulense strain EPM10906 16S ribosomal RNA gene, partial sequence	KM973037	1384	GenBank ENA	1578165 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #67770]	Mycobacteroides saopaulense EPM10906	GCA_001456355	complete	GenBank ENA	1578165 tax ID
[Ref.: #67770]	Mycobacteroides saopaulense CCUG 66554	GCA_002086715	contig	GenBank ENA	1578165 tax ID
[Ref.: #66792]	Mycobacterium saopaulense strain CCUG 66554	1578165.5	wgs	PATRIC	1578165 tax ID	*

[Ref.: #67770]

GC-content

64.6 mol%

high performance liquid chromatography (HPLC)

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Mycobacterium saopaulense strain CCUG 66554 PATRIC: 1578165.5
[Ref.: #125438]	gram-positive	gram-positiveⓘ	yes	89.822	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	96.852	yes
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	61.302	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	82.367	yes
[Ref.: #125438]	thermophile	thermophilicⓘ	no	95.4	no
[Ref.: #125438]	motile2+	flagellatedⓘ	no	90.5	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Mycobacteroides saopaulense CCUG 66554 NCBI: GCA_002086715
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	88.4
[Ref.: #125439]	BacteriaNet	motilityⓘ	no	56.6
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	positive	86.8
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate aerobe	91.5

Availability in culture collections External links

[Ref.: #63351]

Culture collection no.

CCUG 66554, LMG 28586, JCM 32436, INCQS 0733

[Ref.: #110063]

SI-ID 396484

References

#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 )

#63351 Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 66554

#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) .

#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 .

#110063

Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.: StrainInfo: A central database for resolving microbial strain identifiers . ( DOI 10.60712/SI-ID396484.1 )

#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 .

* These data were automatically processed and therefore are not curated

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Mycobacterium saopaulense strain CCUG 66554

Mycobacteroides saopaulense CCUG 66554

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