Methylosinus sp. (DSM 24478, LW3)

Name: Methylosinus sp. (DSM 24478, LW3)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 158080

Type strain

Strain Designation LW3

Culture col. no. DSM 24478 LW3

NCBI tax ID(s) 427

Special Collections DSMZ

History <- A. Vorobev, Univ. of Washington, Dept. of Chemical Eng., Seattle, USA; LW3 <- M. G. Kalyuzhnaya <- A. J. Auman

Links

LW3 is a bacterium that was isolated from Sediment.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 397225

DSM 24478

@ref 20215

Last LPSN update 2026-02-09 11:38:13

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Methylocystaceae

Genus Methylosinus

Full scientific name Methylosinus (ex Whittenbury et al. 1970) Bowman et al. 1993

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	97
125439	negative	98

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
64654	MINERAL MEDIUM (DSMZ Medium 1007)		Medium recipe at MediaDive	Name: MINERAL MEDIUM (DSMZ Medium 1007) Composition: KNO3 0.25 g/l KH2PO4 0.1 g/l MgSO4 x 7 H2O 0.05 g/l CaCl2 x 2 H2O 0.01 g/l EDTA 0.005 g/l FeSO4 x 7 H2O 0.002 g/l CoCl2 x 6 H2O 0.0002 g/l CuCl2 x 5 H2O 0.0001 g/l ZnSO4 x 7 H2O 0.0001 g/l Na2MoO4 3e-05 g/l MnCl2 x 4 H2O 3e-05 g/l NiCl2 x 6 H2O 2e-05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
64654	positive	growth	25

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	aerobe	97.4

Spore formation

@ref	Spore formation	Confidence
125438		90.339
125439		94.9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Sediment
#Environmental	#Terrestrial	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
64654	Sediment	Lake Washington	USA	USA	North America

Taxonmaps

69479

Global distribution of 16S sequence AF150788 (>99% sequence identity) for Methylosinus from Microbeatlas

Aquatic Samples	3886
Soil Samples	930
Animal Samples	346
Plant Samples	399
Total Samples	5561

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM52711v1 assembly for Methylosinus sp. LW3	scaffold	GCA_000527115	107635.3	2517093011	107635	77.17

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
64654	Methylosinus sp. LW3 16S ribosomal RNA gene, partial sequence	AF150788	1448		107635

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Methylosinus sp. LW3
NCBI: GCA_000527115

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	97.40	no
125439	gram_stain	BacteriaNetⓘ	negative	98.00	no
125439	motility	BacteriaNetⓘ	yes	52.00	no
125439	spore_formation	BacteriaNetⓘ	no	94.90	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methylosinus sp. LW3 LW3
NCBI: GCA_000527115.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.00	no
125438	anaerobic	anaerobicⓘ	no	82.37	no
125438	aerobic	aerobicⓘ	yes	76.46	no
125438	spore-forming	spore-formingⓘ	no	90.34	no
125438	thermophilic	thermophileⓘ	no	96.12	yes
125438	flagellated	motile2+ⓘ	yes	67.11	no

Literature

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 )
#64654	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 24478
#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 .
#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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