Priestia abyssalis (DSM 25875, CCTCC AB 2012074, NBRC 109102)

Name: Priestia abyssalis (DSM 25875, CCTCC AB 2012074, NBRC 109102)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 22926

Type strain

Culture col. no. DSM 25875 CCTCC AB 2012074 NBRC 109102 SCSIO 15042

NCBI tax ID(s) 1221450

Special Collections DSMZ

Links

Priestia abyssalis DSM 25875 is a mesophilic prokaryote that was isolated from deep sea sediment.

mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 390794

DSM 25875,NBRC 109102

@ref 20215

Last LPSN update 2025-12-16 09:52:32

Domain Bacillati

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Bacillaceae

Genus Priestia

Species Priestia abyssalis

Full scientific name Priestia abyssalis (You et al. 2013) Gupta et al. 2020

Synonyms (1)

Bacillus abyssalis

Morphology

Cell morphology

@ref	Motility	Confidence
125439		90.8

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
19316	BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514)		Medium recipe at MediaDive	Name: BACTO MARINE BROTH (DIFCO 2216) (DSMZ Medium 514) Composition: NaCl 19.45 g/l MgCl2 5.9 g/l Bacto peptone 5.0 g/l Na2SO4 3.24 g/l CaCl2 1.8 g/l Yeast extract 1.0 g/l KCl 0.55 g/l NaHCO3 0.16 g/l Fe(III) citrate 0.1 g/l KBr 0.08 g/l SrCl2 0.034 g/l H3BO3 0.022 g/l Na2HPO4 0.008 g/l Na-silicate 0.004 g/l NaF 0.0024 g/l (NH4)NO3 0.0016 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
19316	positive	growth	28	mesophilic

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		97.2

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Marine
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
19316	deep sea sediment	South China Sea (112° 30.203 E, 18°1.654 N) at a depth of 2439 m	China	CHN	Asia	18.0275	112.503 18.0275/112.503

Taxonmaps

69479

Global distribution of 16S sequence JX232168 (>99% sequence identity) for Priestia abyssalis from Microbeatlas

Aquatic Samples	553
Soil Samples	2068
Animal Samples	292
Plant Samples	753
Total Samples	3666

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
19316	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	ASM201959v1 assembly for Priestia abyssalis DSM 25875	scaffold	GCA_002019595	1221450.3	2997781242	1221450	72.2

Genome browser: GCA_002019595

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
19316	Bacillus abyssalis strain SCSIO 15042 16S ribosomal RNA gene, partial sequence	JX232168	1405		1221450
124043	Priestia abyssalis strain SCSIO 15042 16S ribosomal RNA gene, partial sequence.	PP038289	831		1221450

GC content

@ref	GC-content (mol%)	Method
19316	43.1	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: Priestia abyssalis DSM 25875
NCBI: GCA_002019595

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	97.20	no
125439	motility	BacteriaNetⓘ	yes	90.80	no
125439	gram_stain	BacteriaNetⓘ	variable	80.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	85.70	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Priestia abyssalis DSM 25875
NCBI: GCA_002019595.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	70.85	no
125438	anaerobic	anaerobicⓘ	no	92.19	no
125438	aerobic	aerobicⓘ	yes	74.35	no
125438	spore-forming	spore-formingⓘ	yes	87.89	no
125438	thermophilic	thermophileⓘ	no	92.53	yes
125438	flagellated	motile2+ⓘ	yes	81.58	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Enzymology	Unveiling novel features and phylogenomic assessment of indigenous Priestia megaterium AB-S79 using comparative genomics.	Adeniji AA, Chukwuneme CF, Conceicao EC, Ayangbenro AS, Wilkinson E, Maasdorp E, de Oliveira T, Babalola OO.	Microbiol Spectr	10.1128/spectrum.01466-24	2025
Genetics	From the depths of the Java Trench: genomic analysis of Priestia flexa JT4 reveals bioprospecting and lycopene production potential.	Radjasa OK, Steven R, Natanael Y, Nugrahapraja H, Radjasa SK, Kristianti T, Moeis MR, Trinugroho JP, Suharya HB, Rachmatsyah AO, Dwijayanti A, Putri MR, de Fretes CE, Siallagan ZL, Fadli M, Opier RDA, Farahyah JD, Rahmawati V, Rizanti M, Humaira Z, Prihatmanto AS, Hananto ND, Susanto RD, Chahyadi A, Elfahmi, Priharto N, Kamarisima, Dwivany FM.	BMC Genomics	10.1186/s12864-024-11115-2	2024
Pathogenicity	Isolation, Identification and Prevention of Bacterial Spot Disease on Grifola frondosa.	Ge JT, Rong N, Li JZ, Lu YY, Tao SY, Ye XR, Cheng JX, Wang JQ, Zhang B, Li Y, Hu JJ.	J Fungi (Basel)	10.3390/jof11110777	2025
Phylogeny	Bacillus abyssalis sp. nov., isolated from a sediment of the South China Sea.	You ZQ, Li J, Qin S, Tian XP, Wang FZ, Zhang S, Li WJ	Antonie Van Leeuwenhoek	10.1007/s10482-013-9875-7	2013
Phylogeny	Bacillus thaonhiensis sp. nov., a new species, was isolated from the forest soil of Kyonggi University by using a modified culture method.	Van Pham HT, Kim J	Curr Microbiol	10.1007/s00284-013-0443-1	2013

References

#19316	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 25875
#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) .
#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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Priestia abyssalis (DSM 25875, CCTCC AB 2012074, NBRC 109102)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_002019595

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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