Lysobacter arseniciresistens (DSM 27222, CGMCC 1.10752, KCTC 23365)

Name: Lysobacter arseniciresistens (DSM 27222, CGMCC 1.10752, KCTC 23365)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 23344

Type strain

Strain Designation ZS79

Culture col. no. DSM 27222 CGMCC 1.10752 KCTC 23365 ZS79

NCBI tax ID(s) 1385522

Special Collections DSMZ KCTC Literature strains

History DSMZ <- KCTC <- G. Wang, Nat. Key Laboratory of Agricultural Microbiology, Hubei, Wuhan

Links

Lysobacter arseniciresistens ZS79 is an aerobe, Gram-negative, motile bacterium that was isolated from subsurface soil of Tieshan iron mine.

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

Name and taxonomic classification

SI-ID 399943

KCTC 23365,DSM 27222

@ref 20215

Last LPSN update 2026-05-29 08:30:26

Domain Bacteria

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Lysobacterales

Family Lysobacteraceae

Genus Lysobacter

Species Lysobacter arseniciresistens

Full scientific name Lysobacter arseniciresistens Luo et al. 2012

Synonyms (1)

Novilysobacter arseniciresistens

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility	Confidence
30361	negative	1.9 µm	0.4 µm	rod-shaped
125438	negative					98.25

Pigmentation

30361

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
19157	REACTIVATION WITH LIQUID MEDIUM 830 (DSMZ Medium 830c)		Medium recipe at MediaDive	Name: REACTIVATION WITH LIQUID MEDIUM 830 (DSMZ Medium 830c) Composition: Agar 15.0 g/l Yeast extract 0.5 g/l Proteose peptone 0.5 g/l Casamino acids 0.5 g/l Glucose 0.5 g/l Starch 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)
19157	positive	growth	28
30361	positive	growth	04-37
30361	positive	optimum	28

Culture pH

@ref	Ability	Type	PH	PH range
30361	positive	optimum	7
30361	positive	growth	05-09	alkaliphile

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
30361	aerobe
125439	obligate aerobe	98.906

Spore formation

@ref	Spore formation	Confidence
125439		98.634
125438		92.72

Halophily

@ref	Salt	Growth	Tested relation	Concentration
30361	NaCl	positive	growth	0-4 %

Metabolite production

@ref	Chebi-ID	Metabolite	Production
30361	16136 ChEBI	hydrogen sulfide

Enzymes

@ref	Value	Activity	Ec
30361	acid phosphatase	+	3.1.3.2
30361	alkaline phosphatase	+	3.1.3.1
30361	catalase	+	1.11.1.6
30361	cytochrome oxidase	+	1.9.3.1
30361	gelatinase	+

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Other	#Mine
#Environmental	#Terrestrial	#Soil

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
19157	subsurface soil of Tieshan iron mine	Hubei Province, Daye City	China	CHN	Asia	30.2072	114.901 30.2072/114.901

Taxonmaps

69479

Global distribution of 16S sequence HQ315827 (>99% sequence identity) for Lysobacter arseniciresistens from Microbeatlas

Aquatic Samples	23
Soil Samples	171
Animal Samples	62
Plant Samples	8
Total Samples	264

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
19157	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	ASM76833v1 assembly for Lysobacter arseniciresistens ZS79	contig	GCA_000768335	913325.4	2634166449	913325	58.71

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
19157	Lysobacter arseniciresistens ZS79 16S ribosomal RNA gene, partial sequence	HQ315827	1466		913325

GC content

19157

GC-content (mol%)70.7

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM76833v1 assembly for Lysobacter arseniciresistens ZS79
NCBI: GCA_000768335

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	gram_stain	BacteriaNetⓘ	negative	74.83	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.91	no
125439	motility	BacteriaNetⓘ	yes	67.67	no
125439	spore_formation	BacteriaNetⓘ	no	98.63	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Lysobacter arseniciresistens ZS79
PATRIC: 913325.4

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.25	yes
125438	anaerobic	anaerobicⓘ	no	97.26	yes
125438	aerobic	aerobicⓘ	yes	84.77	no
125438	spore-forming	spore-formingⓘ	no	92.72	no
125438	thermophilic	thermophileⓘ	no	93.35	yes
125438	flagellated	motile2+ⓘ	no	52.44	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Genomic information of the arsenic-resistant bacterium Lysobacter arseniciresistens type strain ZS79(T) and comparison of Lysobacter draft genomes.	Liu L, Zhang S, Luo M, Wang G.	Stand Genomic Sci	10.1186/s40793-015-0070-5	2015
Phylogeny	Lysobacter zhanggongensis sp. nov. Isolated from a Pit Mud.	Zhang XF, Wang HH, Sun XY, Pan CM	Curr Microbiol	10.1007/s00284-017-1330-y	2017
Phylogeny	Lysobacter arseniciresistens sp. nov., an arsenite-resistant bacterium isolated from iron-mined soil.	Luo G, Shi Z, Wang G	Int J Syst Evol Microbiol	10.1099/ijs.0.034405-0	2011
Phylogeny	Lysobacter selenitireducens sp. nov., isolated from river sediment.	Mao S, Li S, Guo B, Mu W, Hou X, Liu H, Wei S, Liu A, Kong L, Chen Z	Int J Syst Evol Microbiol	10.1099/ijsem.0.005550	2022

References

#19157	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 27222
#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 )
#26701	IJSEM 1659 2012 ( DOI 10.1099/ijs.0.034405-0 , PubMed 21890727 )
#30361	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 #26701
#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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Lysobacter arseniciresistens (DSM 27222, CGMCC 1.10752, KCTC 23365)

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 production

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000768335

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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