Borrelia afzelii (DSM 10508, ATCC 51567, CIP 103469)

Name: Borrelia afzelii (DSM 10508, ATCC 51567, CIP 103469)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 14305

Type strain

Strain Designation VS 461

Culture col. no. DSM 10508 ATCC 51567 CIP 103469 VS 461

NCBI tax ID(s) 29518

Special Collections DSMZ CIP

History <- Collection des Bactéries de l' Institut Pasteur; CIP 103469 <- G. Baranton; <- O. Péter; VS 461

Links

Borrelia afzelii VS 461 is a microaerophile, mesophilic prokaryote that was isolated from Ixodes ricinus.

microaerophile mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 93003

ATCC 51992,ATCC 51567,CIP 103469,DSM 10508,BCRC 17181,CCRC 17181

@ref 20215

Last LPSN update 2026-02-09 11:21:23

Domain Bacteria

Phylum Spirochaetota

Class Spirochaetia

Order Spirochaetales

Family Borreliaceae

Genus Borrelia

Species Borrelia afzelii

Full scientific name Borrelia afzelii Canica et al. 1994

Synonyms (1)

Borreliella afzelii

BacDive ID	Other strains from **Borrelia afzelii** (1)	Type strain
14306	B. afzelii P-Ko/1984, DSM 16073

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	100

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
3953	BSK-MEDIUM (DSMZ Medium 403)		Medium recipe at MediaDive	Name: BSK-MEDIUM (DSMZ Medium 403) Composition: Bovine serum albumin 35.0245 g/l Gelatine 9.79706 g/l HEPES buffer 4.19874 g/l Proteose peptone no. 2 3.49895 g/l Glucose 2.09937 g/l Proteose yeastolate 0.69979 g/l Tryptone peptone 0.69979 g/l Na-pyruvate 0.559832 g/l Sodium citrate 0.489853 g/l N-Acetylglucosamine 0.279916 g/l MgCl2 x 6 H2O 0.209937 g/l L-Glutamine 0.069979 g/l Double distilled water CMRL 1066 Rabbit serum

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
3953	positive	growth	37	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
3953	microaerophile
125439	facultative anaerobe	99.7

Spore formation

@ref	Spore formation	Confidence
125439		100
125438		94.864

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Arthropoda	#Tick

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
3953	Ixodes ricinus	Ixodes ricinus	Sion	Switzerland	CHE	Europe

Taxonmaps

69479

Global distribution of 16S sequence FR733687 (>99% sequence identity) for Borreliella from Microbeatlas

Aquatic Samples	31
Soil Samples	26
Animal Samples	197
Plant Samples
Total Samples	254

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
3953	2	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	ASM1420229v1 assembly for Borreliella afzelii DSM 10508	scaffold	GCA_014202295	29518.17	2861437327	29518	68.56

16S sequences

@ref	Description	Accession	Length	NCBI tax ID
20218	Borrelia afzelii strain BO23 16S ribosomal RNA (rrs) gene, partial sequence	DQ111061	554	29518
20218	Borrelia afzelii strain BO23 16S ribosomal RNA gene, partial sequence	KF607014	391	29518
20218	Borrelia afzelii strain VS461 16S ribosomal RNA gene, partial sequence	AY342034	1013	29518
20218	Borreliella afzelii strain VS461 16S ribosomal RNA gene, partial sequence	U78151	781	29518
3953	Borrelia afzelii partial 16S rRNA gene, type strain DSM10508T	FR733687	1524	29518

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Borreliella afzelii DSM 10508
NCBI: GCA_014202295

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	100.00	no
125439	motility	BacteriaNetⓘ	yes	69.50	no
125439	gram_stain	BacteriaNetⓘ	negative	100.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	99.70	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Borreliella afzelii DSM 10508
NCBI: GCA_014202295.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	81.58	no
125438	anaerobic	anaerobicⓘ	no	82.02	no
125438	aerobic	aerobicⓘ	no	94.42	no
125438	spore-forming	spore-formingⓘ	no	94.86	no
125438	thermophilic	thermophileⓘ	no	88.70	no
125438	flagellated	motile2+ⓘ	yes	51.95	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Combining citizen science and molecular diagnostic methods to investigate the prevalence of Borrelia burgdorferi s.l. and Borrelia miyamotoi in tick pools across Great Britain.	Shan J, Jia Y, Hickenbotham P, Teulieres L, Clokie MRJ.	Front Microbiol	10.3389/fmicb.2023.1126498	2023
Phylogeny	The genus Borrelia reloaded.	Margos G, Gofton A, Wibberg D, Dangel A, Marosevic D, Loh SM, Oskam C, Fingerle V.	PLoS One	10.1371/journal.pone.0208432	2018
	Targeting Multicopy Prophage Genes for the Increased Detection of Borrelia burgdorferi Sensu Lato (s.l.), the Causative Agents of Lyme Disease, in Blood.	Shan J, Jia Y, Teulieres L, Patel F, Clokie MRJ.	Front Microbiol	10.3389/fmicb.2021.651217	2021

References

#3953	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 10508
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#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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Borrelia afzelii (DSM 10508, ATCC 51567, CIP 103469)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_014202295

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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