Acinetobacter radioresistens (CIP 110469, NIPH 2130, SEIP 12.81)

Name: Acinetobacter radioresistens (CIP 110469, NIPH 2130, SEIP 12.81)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 136812

Type strain

Strain Designation SEIP 12.81

Culture col. no. CIP 110469 NIPH 2130 SEIP 12.81 BOUVET 76

NCBI tax ID(s) 1217674 40216

Special Collections CIP

Links

Acinetobacter radioresistens SEIP 12.81 is a mesophilic, Gram-negative, oval-shaped prokaryote that was isolated from Human, Urine.

Gram-negative oval-shaped mesophilic genome sequence

Name and taxonomic classification

SI-ID 413993

CIP 110469

@ref 20215

Last LPSN update 2025-12-16 10:02:40

Domain Pseudomonadati

Phylum Pseudomonadota

Class Gammaproteobacteria

Order Pseudomonadales

Family Moraxellaceae

Genus Acinetobacter

Species Acinetobacter radioresistens

Full scientific name Acinetobacter radioresistens Nishimura et al. 1988

BacDive ID	Other strains from **Acinetobacter radioresistens** (35)	Type strain
8096	A. radioresistens FO-1, DSM 6976, ATCC 43998, IAM 13186, CCUG ... (type strain)
100218	A. radioresistens ST033182(HKI),
100219	A. radioresistens ST027326(HKI), F158, Nü49,
147887	A. radioresistens CCUG 34434
155470	A. radioresistens CCUG 57822
155574	A. radioresistens CCUG 58199
155608	A. radioresistens CCUG 58362
155713	A. radioresistens CCUG 59117
155833	A. radioresistens CCUG 59434
155952	A. radioresistens CCUG 59753
156455	A. radioresistens CCUG 61887
166022	A. radioresistens JCM 9327, ATCC 43999, IAM 13187
166023	A. radioresistens JCM 9328, ATCC 49000, IAM 13188
169349	A. radioresistens 108289, DSM 108289
169350	A. radioresistens 108290, DSM 108290
169351	A. radioresistens 108291, DSM 108291
169352	A. radioresistens 108292, DSM 108292
169353	A. radioresistens 108293, DSM 108293
169354	A. radioresistens 108294, DSM 108294
169355	A. radioresistens 108295, DSM 108295
169356	A. radioresistens 108296, DSM 108296
169357	A. radioresistens 22290, 108297, DSM 108297
169358	A. radioresistens 108349, DSM 108349
169359	A. radioresistens 108719, DSM 108719
169360	A. radioresistens 108820, DSM 108820
169361	A. radioresistens 109999, DSM 109999
169362	A. radioresistens 112286, DSM 112286
169363	A. radioresistens 112285, DSM 112285
173525	A. radioresistens CM38.2, CIP 111668
173526	A. radioresistens CM38.1, CIP 111749
174835	A. radioresistens 115027, DSM 115027
174836	A. radioresistens Dres_AcRes_20, DSM 113703
174837	A. radioresistens Dres_AcRes_18, DSM 110647
174838	A. radioresistens Dres_AcRes_19, DSM 110746
174839	A. radioresistens 115092, DSM 115092

Morphology

Cell morphology

@ref	Gram stain	Cell shape	Motility	Confidence
125438	negative			98.667
36467	negative	oval-shaped

Culture and growth conditions

Culture medium

@ref	Name	Growth	Composition	Medium link
36467	MEDIUM 72- for trypto casein soja agar		Distilled water make up to (1000.000 ml);Trypto casein soy agar (40.000 g)
36467	CIP Medium 72			Medium recipe at CIP

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
36467	positive	growth	30	mesophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	95.9

Spore formation

@ref	Spore formation	Confidence
125438		94.793
125439		92.6

Isolation, sampling and environmental information

Isolation

36467

Sample typeHuman, Urine

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
36467	1	Risk group (French classification)

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	Acinetobacter radioresistens NIPH 2130	contig		1217674.3		1217674	76.7
66792	Acin_radi_NIPH_2130_V1 assembly for Acinetobacter radioresistens NIPH 2130	scaffold	GCA_000368885	1217674.4	2529293102	1217674	76.61

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acinetobacter radioresistens NIPH 2130
NCBI: GCA_000368885

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	95.90	no
125439	gram_stain	BacteriaNetⓘ	negative	81.80	no
125439	motility	BacteriaNetⓘ	yes	53.10	no
125439	spore_formation	BacteriaNetⓘ	no	92.60	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acinetobacter radioresistens NIPH 2130 NIPH 2130
NCBI: GCA_000368885.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.67	no
125438	anaerobic	anaerobicⓘ	no	95.56	no
125438	aerobic	aerobicⓘ	yes	88.02	no
125438	spore-forming	spore-formingⓘ	no	94.79	no
125438	thermophilic	thermophileⓘ	no	97.48	yes
125438	flagellated	motile2+ⓘ	no	80.84	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Molecular Epidemiology of Carbapenem-Resistant Acinetobacter baumannii Isolates from Northern Africa and the Middle East.	Higgins PG, Hagen RM, Kreikemeyer B, Warnke P, Podbielski A, Frickmann H, Loderstadt U.	Antibiotics (Basel)	10.3390/antibiotics10030291	2021
Genetics	Comparative genomic analysis of Acinetobacter strains isolated from murine colonic crypts.	Saffarian A, Touchon M, Mulet C, Tournebize R, Passet V, Brisse S, Rocha EPC, Sansonetti PJ, Pedron T.	BMC Genomics	10.1186/s12864-017-3925-x	2017
Metabolism	Escherichia coli Overexpressing a Baeyer-Villiger Monooxygenase from Acinetobacter radioresistens Becomes Resistant to Imipenem.	Minerdi D, Zgrablic I, Castrignano S, Catucci G, Medana C, Terlizzi ME, Gribaudo G, Gilardi G, Sadeghi SJ.	Antimicrob Agents Chemother	10.1128/aac.01088-15	2016

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 )
#36467	Collection of Institut Pasteur ; Curators of the CIP; CIP 110469
#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) .
#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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Acinetobacter radioresistens (CIP 110469, NIPH 2130, SEIP 12.81)

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

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000368885

Genome-based predictions

Predictions

Literature

Literature

References

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