Mycobacterium longobardum (DSM 45394, CCUG 58460, FI-07034)

Name: Mycobacterium longobardum (DSM 45394, CCUG 58460, FI-07034)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 8346

Type strain

Strain Designation FI-07034

Culture col. no. DSM 45394 CCUG 58460 FI-07034 JCM 32217

NCBI tax ID(s) 1108812

Special Collections DSMZ CCUG JCM Literature strains

History <- E. Tortoli, Firenze, Italy; FI-07034 <- V. Penati <- S. Pierdomenico CCUG 58460 <-- E. Tortoli; Careggi Hosp., Italy; FI-07034 <-- V. Penati <-- S. Pierdomenico.

Links

Mycobacterium longobardum FI-07034 is a microaerophile, mesophilic, coccus-shaped prokaryote that was isolated from sputum from an Italian patient suffering from broncho-pneumonitis.

coccus-shaped microaerophile mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 407449

CT2017-00315,DSM 45394,CCUG 58460

@ref 20215

Last LPSN update 2025-12-16 09:48:49

Domain Bacillati

Phylum Actinomycetota

Class Actinomycetes

Order Mycobacteriales

Family Mycobacteriaceae

Genus Mycobacterium

Species Mycobacterium longobardum

Full scientific name Mycobacterium longobardum Tortoli et al. 2013

Synonyms (1)

Mycolicibacter longobardus

Morphology

Cell morphology

@ref	Confidence	Cell shape	Gram stain
30561		coccus-shaped
125438	90.5
125439	96.2		positive

Colony morphology

61692

Incubation period3-5 days

Pigmentation

30561

Productionno

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
16583	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)	Range
16583	positive	growth	37	mesophilic
30561	positive	growth	25-37	mesophilic
30561	positive	optimum	31	mesophilic
67770	positive	growth	37	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
61692	microaerophile
125439	obligate aerobe	98.5

Spore formation

30561

Spore formationno

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30561	17632 ChEBI	nitrate	+	reduction

Enzymes

@ref	Value	Activity	Ec
30561	arylsulfatase	+	3.1.6.1
30561	catalase	+	1.11.1.6

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Infection	#Inflammation	-
#Infection	#Patient	-
#Host Body Product	#Fluids	#Sputum

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
16583	sputum from an Italian patient suffering from broncho-pneumonitis	Lombardy	Italy	ITA	Europe
61692	Human clinical sample		Italy	ITA	Europe
67770	Sputum from an Italian patient suffering from broncho-pneumonitis	Lombardy	Italy	ITA	Europe

Interaction and safety

Risk assessment

@ref	Pathogenicity human	Biosafety level	Biosafety level comment
16583	yes, in single cases	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	ASM2582313v1 assembly for Mycolicibacter longobardus DSM 45394	scaffold	GCA_025823135	1108812.15	8115858300	1108812	70.66
67770	ASM210226v1 assembly for Mycolicibacter longobardus DSM 45394	contig	GCA_002102265	1108812.3	2865385185	1108812	65.54

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
16583	Mycobacterium longobardum strain DSM 45394 16S ribosomal RNA gene, partial sequence	JN571166	1500		1108812

GC content

@ref	GC-content (mol%)	Method
67770	67.9	genome sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Mycolicibacter longobardus DSM 45394
NCBI: GCA_025823135

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.50	no
125439	gram_stain	BacteriaNetⓘ	positive	96.20	no
125439	motility	BacteriaNetⓘ	no	81.10	no
125439	spore_formation	BacteriaNetⓘ	yes	61.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Mycobacterium longobardum strain DSM 45394
PATRIC: 1108812.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	88.66	no
125438	anaerobic	anaerobicⓘ	no	94.51	yes
125438	spore-forming	spore-formingⓘ	no	67.74	yes
125438	aerobic	aerobicⓘ	yes	81.32	no
125438	thermophilic	thermophileⓘ	no	96.40	no
125438	flagellated	motile2+ⓘ	no	90.50	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Phylogenomics and Comparative Genomic Studies Robustly Support Division of the Genus Mycobacterium into an Emended Genus Mycobacterium and Four Novel Genera.	Gupta RS, Lo B, Son J.	Front Microbiol	10.3389/fmicb.2018.00067	2018
Phylogeny	Microbiological features and clinical relevance of new species of the genus Mycobacterium.	Tortoli E.	Clin Microbiol Rev	10.1128/cmr.00035-14	2014
Phylogeny	Survey of 150 strains belonging to the Mycobacterium terrae complex and description of Mycobacterium engbaekii sp. nov., Mycobacterium heraklionense sp. nov. and Mycobacterium longobardum sp. nov.	Tortoli E, Gitti Z, Klenk HP, Lauria S, Mannino R, Mantegani P, Mariottini A, Neonakis I	Int J Syst Evol Microbiol	10.1099/ijs.0.038737-0	2012

References

#16583	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 45394
#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 )
#26892	IJSEM 401 2013 ( DOI 10.1099/ijs.0.038737-0 , PubMed 22447702 )
#30561	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #26892
#61692	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 58460
#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;
#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 longobardum (DSM 45394, CCUG 58460, FI-07034)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_025823135

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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