Sphingomonas alpina (DSM 22537, LMG 26055, S8-3)

Name: Sphingomonas alpina (DSM 22537, LMG 26055, S8-3)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 14269

Type strain

Strain Designation S8-3

Culture col. no. DSM 22537 LMG 26055 S8-3

NCBI tax ID(s) 653931

Special Collections DSMZ Literature strains

Links

Sphingomonas alpina S8-3 is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from alpine soil.

Gram-negative motile rod-shaped aerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 370924

LMG 26055,DSM 22537

@ref 20215

Last LPSN update 2025-12-16 09:50:18

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Sphingomonadales

Family Sphingomonadaceae

Genus Sphingomonas

Species Sphingomonas alpina

Full scientific name Sphingomonas alpina Margesin et al. 2012

Synonyms (1)

Parasphingomonas alpina

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
30431	negative	1.2 µm	0.7 µm	rod-shaped
125439	negative				99.1
125438	negative				94.125

Pigmentation

30431

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
16384	R2A MEDIUM (DSMZ Medium 830)		Medium recipe at MediaDive	Name: R2A MEDIUM (DSMZ Medium 830) Composition: Agar 15.0 g/l Casamino acids 0.5 g/l Starch 0.5 g/l Glucose 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
16384	positive	growth	25	mesophilic
16384	positive	growth	28	mesophilic
30431	positive	growth	1-25

Culture pH

@ref	Ability	Type	PH	PH range
30431	positive	optimum	7.5
30431	positive	growth	06-10	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
30431	aerobe
125439	obligate aerobe	99.4

Spore formation

@ref	Spore formation	Confidence
125439		95.9
125438		91.878

Halophily

@ref	Salt	Growth	Tested relation	Concentration
30431	NaCl	positive	growth	0-1 %

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
30431	22599 ChEBI	arabinose	+	carbon source
30431	4853 ChEBI	esculin	+	hydrolysis
30431	17234 ChEBI	glucose	+	carbon source
30431	25115 ChEBI	malate	+	carbon source
30431	17306 ChEBI	maltose	+	carbon source
30431	37684 ChEBI	mannose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
30431	acid phosphatase	+	3.1.3.2
30431	alkaline phosphatase	+	3.1.3.1
30431	catalase	+	1.11.1.6
30431	cytochrome oxidase	+	1.9.3.1
30431	gelatinase	+

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil
#Climate	#Cold	#Alpine

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
16384	alpine soil	Grossglockner, Hohe Tauern	Austria	AUT	Europe

Taxonmaps

69479

Global distribution of 16S sequence GQ161989 (>99% sequence identity) for Sphingomonas alpina subclade from Microbeatlas

Aquatic Samples	86
Soil Samples	156
Animal Samples	163
Plant Samples	225
Total Samples	630

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
16384	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	ASM1449066v1 assembly for Sphingomonas alpina DSM 22537	complete	GCA_014490665	653931.5	2989653537	653931	95.2
66792	ASM2537009v1 assembly for Sphingomonas alpina DSM 22537	contig	GCA_025370095	653931.9	8049336959	653931	74.38

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
16384	Sphingomonas alpina strain S8-3 16S ribosomal RNA gene, partial sequence	GQ161989	1481		653931
124043	Sphingomonas alpina strain DSM 22537 16S ribosomal RNA gene, partial sequence.	MT821524	676		653931

GC content

@ref	GC-content (mol%)	Method
16384	64.1	high performance liquid chromatography (HPLC)
30431	64.1

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Sphingomonas alpina DSM 22537
NCBI: GCA_014490665

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	95.90	no
125439	motility	BacteriaNetⓘ	yes	57.80	no
125439	gram_stain	BacteriaNetⓘ	negative	99.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Sphingomonas alpina DSM 22537
NCBI: GCA_014490665.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	94.13	no
125438	anaerobic	anaerobicⓘ	no	96.30	yes
125438	aerobic	aerobicⓘ	yes	89.46	no
125438	spore-forming	spore-formingⓘ	no	91.88	no
125438	thermophilic	thermophileⓘ	no	97.89	yes
125438	flagellated	motile2+ⓘ	yes	71.31	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Oxford Nanopore long read-based shotgun metagenomic data sets of simulated bacterial communities originating from fresh spinach and surface water.	Chen Z, Grim CJ, Ramachandran P, Meng J.	Microbiol Resour Announc	10.1128/mra.00586-24	2024
Genetics	Illumina short read-based shotgun metagenomic data sets of simulated bacterial communities derived from fresh spinach and surface water.	Chen Z, Meng J.	Microbiol Resour Announc	10.1128/mra.00375-24	2024
Genetics	Crude Oil Degradation in Temperatures Below the Freezing Point by Bacteria from Hydrocarbon-Contaminated Arctic Soils and the Genome Analysis of Sphingomonas sp. AR_OL41.	Semenova EM, Tourova TP, Babich TL, Logvinova EY, Sokolova DS, Loiko NG, Myazin VA, Korneykova MV, Mardanov AV, Nazina TN.	Microorganisms	10.3390/microorganisms12010079	2023
Genetics	Antarctic Sphingomonas sp. So64.6b showed evolutive divergence within its genus, including new biosynthetic gene clusters.	Nunez-Montero K, Rojas-Villalta D, Barrientos L.	Front Microbiol	10.3389/fmicb.2022.1007225	2022
	Characterization of soil bacterial, archaeal and fungal communities inhabiting archaeological human-impacted layers at Monte Iato settlement (Sicily, Italy).	Siles JA, Ohlinger B, Cajthaml T, Kistler E, Margesin R.	Sci Rep	10.1038/s41598-018-20347-8	2018
Genetics	A novel UV-resistant bacterium Sphingomonas endolithica sp. nov., and genomic analysis, isolated from the north slope of Mount Everest.	Tian M, Zhang W, Zhang G, Bahadur A, Wu S, Yu X, Wu Y, Jia P, Chen T, Liu G.	Antonie Van Leeuwenhoek	10.1007/s10482-023-01903-3	2023
Phylogeny	Sphingomonas aliaeris sp. nov., a new species isolated from pork steak packed under modified atmosphere.	Heidler von Heilborn D, Reinmuller J, Holzl G, Meier-Kolthoff JP, Woehle C, Marek M, Huttel B, Lipski A	Int J Syst Evol Microbiol	10.1099/ijsem.0.004973	2021
Phylogeny	Sphingomonas qilianensis sp. nov., Isolated from Surface Soil in the Permafrost Region of Qilian Mountains, China.	Piao AL, Feng XM, Nogi Y, Han L, Li Y, Lv J	Curr Microbiol	10.1007/s00284-015-0957-9	2015
Phylogeny	Sphingomonas fonticola sp. nov., isolated from spring water.	Sheu SY, Chen YL, Chen WM	Int J Syst Evol Microbiol	10.1099/ijsem.0.000602	2015
Phylogeny	Sphingomonas alpina sp. nov., a psychrophilic bacterium isolated from alpine soil.	Margesin R, Zhang DC, Busse HJ	Int J Syst Evol Microbiol	10.1099/ijs.0.035964-0	2011

References

#16384	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 22537
#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 )
#26767	IJSEM 1558 2012 ( DOI 10.1099/ijs.0.035964-0 , PubMed 21856979 )
#30431	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 #26767
#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 .
#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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Sphingomonas alpina (DSM 22537, LMG 26055, S8-3)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_014490665

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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