Lysobacter bugurensis (DSM 26007, CCTCC AB 207175, KCTC 23077)

Name: Lysobacter bugurensis (DSM 26007, CCTCC AB 207175, KCTC 23077)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 17501

Type strain

Strain Designation ZLD-29

Culture col. no. DSM 26007 CCTCC AB 207175 KCTC 23077 ZLD-29

NCBI tax ID(s) 543356

Special Collections DSMZ KCTC

History <- KCTC <- C.-X. Fang, Wuhan Univ., China; ZLD-29 <- L Zhang, Northewest A&F Univ., China

Links

Lysobacter bugurensis ZLD-29 is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from soil of arid aerea.

Gram-negative aerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 405922

KCTC 23077,DSM 26007

@ref 20215

Last LPSN update 2026-02-09 11:22:14

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Lysobacterales

Family Lysobacteraceae

Genus Lysobacter

Species Lysobacter bugurensis

Full scientific name Lysobacter bugurensis Zhang et al. 2011

Synonyms (1)

Cognatilysobacter bugurensis

Morphology

Cell morphology

@ref	Gram stain	Confidence
67771	negative
125438	negative	98.736
125439	negative	97.4

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
18149	HALF STRENGTH BACTO MARINE BROTH (DSMZ Medium 514a)		Medium recipe at MediaDive	Name: HALF STRENGTH BACTO MARINE BROTH (DSMZ Medium 514a) Composition: Agar 15.0 g/l NaCl 9.725 g/l MgCl2 2.95 g/l Bacto peptone 2.5 g/l Na2SO4 1.62 g/l CaCl2 0.9 g/l Yeast extract 0.5 g/l KCl 0.275 g/l NaHCO3 0.08 g/l Fe(III) citrate 0.05 g/l KBr 0.04 g/l SrCl2 0.017 g/l H3BO3 0.011 g/l Na2HPO4 0.004 g/l Na-silicate 0.002 g/l NaF 0.0012 g/l (NH4)NO3 0.0008 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
18149	positive	growth	28	mesophilic
67771	positive	growth	30	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
67771	aerobe
125439	obligate aerobe	99.9

Spore formation

@ref	Spore formation	Confidence
125439		95.8

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
18149	soil of arid aerea	Xinjiang Province	China	CHN	Asia	43.2667	89.2333 43.2667/89.2333
67771	From soil	Gobi	China	CHN	Asia

Taxonmaps

69479

Global distribution of 16S sequence EU780693 (>99% sequence identity) for Lysobacter bugurensis subclade from Microbeatlas

Aquatic Samples	90
Soil Samples	423
Animal Samples	448
Plant Samples	74
Total Samples	1035

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
18149	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	ASM1465209v1 assembly for Cognatilysobacter bugurensis KCTC 23077	scaffold	GCA_014652095	543356.4	2995926021	543356	74.32

16S sequences

@ref	Description	Accession	Length	NCBI tax ID
18149	UNVERIFIED_ORG: Lysobacter bugurensis strain ZLD-29 16S ribosomal RNA gene, partial sequence	EU780693	1453	543356
124043	Lysobacter bugurensis strain KCTC 23077 16S ribosomal RNA gene, partial sequence.	MT758092	1361	543356
124043	Lysobacter bugurensis strain KCTC 23077 16S ribosomal RNA gene, partial sequence.	MT759891	1361	543356

GC content

@ref	GC-content (mol%)	Method
18149	68.2	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Lysobacter bugurensis KCTC 23077
NCBI: GCA_014652095

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	95.80	no
125439	motility	BacteriaNetⓘ	yes	67.70	no
125439	gram_stain	BacteriaNetⓘ	negative	97.40	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.90	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Lysobacter bugurensis KCTC 23077
NCBI: GCA_014652095.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.74	yes
125438	anaerobic	anaerobicⓘ	no	96.35	yes
125438	aerobic	aerobicⓘ	yes	79.32	no
125438	spore-forming	spore-formingⓘ	no	88.30	no
125438	thermophilic	thermophileⓘ	no	90.95	no
125438	flagellated	motile2+ⓘ	yes	51.78	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Lysobacter helvus sp. nov. and Lysobacter xanthus sp. nov., isolated from Soil in South Korea.	Kim I, Choi J, Chhetri G, Seo T	Antonie Van Leeuwenhoek	10.1007/s10482-019-01256-w	2019
Phylogeny	Lysobacter mobilis sp. nov., isolated from abandoned lead-zinc ore.	Yang SZ, Feng GD, Zhu HH, Wang YH	Int J Syst Evol Microbiol	10.1099/ijs.0.000026	2014
Phylogeny	Lysobacter korlensis sp. nov. and Lysobacter bugurensis sp. nov., isolated from soil.	Zhang L, Bai J, Wang Y, Wu GL, Dai J, Fang CX	Int J Syst Evol Microbiol	10.1099/ijs.0.024448-0	2010

References

#18149	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 26007
#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 )
#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) .
#67771	Korean Collection for Type Cultures (KCTC) ; Curators of the KCTC;
#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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Lysobacter bugurensis (DSM 26007, CCTCC AB 207175, KCTC 23077)

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_014652095

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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