Name: Microvirga massiliensis JC119
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 132571

Type strain:

Species: Microvirga massiliensis

Strain Designation: JC119

Culture col. no.: DSM 26813, CSUR P153

Strain history: <- J.-C. Lagier, Timone Hospital, Marseille, France; JC119

NCBI tax ID(s): 1033741 (species)

SI-ID 402135

DSM 26813

Special Collections

Section

When using BacDive for research please cite our paper

Microvirga massiliensis JC119 is a mesophilic bacterium that was isolated from human feces, 16-year-old Senegalese man.

mesophilic
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	*Pseudomonadota*
	Class	*Alphaproteobacteria*
	Order	*Hyphomicrobiales*
	Family	*Methylobacteriaceae*
	Genus	*Microvirga*
	Species	**Microvirga massiliensis**
	Full Scientific Name (LPSN)	Microvirga massiliensis Caputo et al. 2016

Information on morphological and physiological properties Morphology

[Ref.: #125438]	Gram stain	negative Confidence in %98.968

Information on culture and growth conditions Culture and growth conditions

[Ref.: #24360]

Culture medium

R2A MEDIUM (DSMZ Medium 830)

[Ref.: #24360]

Culture medium growth

[Ref.: #24360]

Culture medium link

Medium recipe provided by DSMZ

	Temperatures	Kind of temperature		Temperature
[Ref.: #24360]			growth	37 °C

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

[Ref.: #24360]	Sample type/isolated from	human feces, 16-year-old Senegalese man
[Ref.: #24360]	Geographic location (country and/or sea, region)	Sine Saloum, Dielmo
[Ref.: #24360]	Country	Senegal
[Ref.: #24360]	Country ISO 3 Code	SEN
[Ref.: #24360]	Continent	Africa
* marker position based on {}

Isolation sources categories

#Host	#Human	#Male
#Host Body Product	#Gastrointestinal tract	#Feces (Stool)

What are isolation sources categories?

[Ref.: #69479]

Global distribution of 16S sequence JF824802 (>99% sequence identity) for Microvirga massiliensis subclade from Microbeatlas

Aquatic Samples	694
Soil Samples	7646
Animal Samples	1033
Plant Samples	958
Total Samples	10331

Information on possible application of the strain and its possible interaction with e.g. potential hosts Safety information

[Ref.: #24360]

Biosafety level

Risk group (German classification)
According to TRBA 466

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.: #24360]	Microvirga massiliensis strain JC119 16S ribosomal RNA gene, partial sequence	JF824802	1475	GenBank ENA	1033741 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Microvirga massiliensis JC119	GCA_001006805	scaffold	GenBank ENA	1033741 tax ID	*
[Ref.: #66792]	Microvirga massiliensis JC119	GCA_001050495	contig	GenBank ENA	1033741 tax ID	*
[Ref.: #66792]	Microvirga massiliensis JC119	2802429303	draft	JGI IMG/M	1033741 tax ID	*
[Ref.: #66792]	Microvirga massiliensis JC119	2634166464	draft	JGI IMG/M	1033741 tax ID	*

[Ref.: #24360]

GC-content

63.28 mol%

sequence analysis

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: Microvirga massiliensis JC119 NCBI: GCA_001006805.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	no	98.968	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	no	94.725	no
[Ref.: #125438]	aerobic	aerobicⓘ	yes	76.15	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	87.69	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	97.85	no
[Ref.: #125438]	motile2+	flagellatedⓘ	yes	70.6	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Microvirga massiliensis JC119 NCBI: GCA_001006805
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	89.1
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	61
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	negative	83.4
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	aerobe	80.8

Availability in culture collections External links

[Ref.: #24360]

Culture collection no.

DSM 26813, CSUR P153

[Ref.: #90888]

SI-ID 402135

Literature:

Topic	Title	Authors	Journal	DOI	Year
Genetics	Microvirga massiliensis sp. nov., the human commensal with the largest genome.	Caputo A, Lagier JC, Azza S, Robert C, Mouelhi D, Fournier PE, Raoult D	Microbiologyopen	10.1002/mbo3.329	2016	*

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 )

#24360

Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 26813

#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) .

#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 .

#90888

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-ID402135.1 )

#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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