Heyndrickxia vini (ACCC 06413, JCM 19841)

Name: Heyndrickxia vini (ACCC 06413, JCM 19841)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 162626

Type strain

Culture col. no. ACCC 06413 JCM 19841

NCBI tax ID(s) 1476025

Special Collections JCM

History JCM <-- Z. Ruan; Inst. of Agric. Resour. & Reg. Plann., CAAS, China; LAM0415.

Links

Heyndrickxia vini JCM 19841 is a bacterium that was isolated from Alcohol fermentation pit mud used for Luzhou-flavour liquor production.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 27953

JCM 19841

@ref 20215

Last LPSN update 2026-05-29 08:44:58

Domain Bacteria

Phylum Bacillota

Class Bacilli

Order Caryophanales

Family Bacillaceae

Genus Heyndrickxia

Species Heyndrickxia vini

Full scientific name Heyndrickxia vini (Ma et al. 2017) Gupta et al. 2020

Synonyms (1)

Bacillus vini

Morphology

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
67770	positive	growth	35

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	facultative anaerobe	93.307

Spore formation

@ref	Spore formation	Confidence
125438		91.82

Observation

67770

Observationquinones: MK-7, MK-8

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
67770	Alcohol fermentation pit mud used for Luzhou-flavour liquor production	Luzhou-flavour	Sichuan Province	China	CHN	Asia

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM1677227v1 assembly for Heyndrickxia vini JCM 19841	complete	GCA_016772275	1476025.3	2994829939	1476025	98.57

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Heyndrickxia vini strain LAM0415 16S ribosomal RNA gene, partial sequence	KJ005123	1460		1476025

GC content

@ref	GC-content (mol%)	Method
67770	34.8	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM1677227v1 assembly for Heyndrickxia vini JCM 19841
BV-BRC: 1476025.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	93.31	no
125439	motility	BacteriaNetⓘ	yes	69.33	no
125439	gram_stain	BacteriaNetⓘ	positive	42.53	no
125439	spore_formation	BacteriaNetⓘ	yes	84.95	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Heyndrickxia vini JCM 19841
NCBI: GCA_016772275.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	66.29	no
125438	anaerobic	anaerobicⓘ	no	96.77	no
125438	aerobic	aerobicⓘ	yes	78.12	no
125438	spore-forming	spore-formingⓘ	yes	91.82	no
125438	thermophilic	thermophileⓘ	no	90.39	yes
125438	flagellated	motile2+ⓘ	yes	82.18	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Taxonomic Evaluation of the Heyndrickxia (Basonym Bacillus) sporothermodurans Group (H. sporothermodurans, H. vini, H. oleronia) Based on Whole Genome Sequences.	Fiedler G, Herbstmann AD, Doll E, Wenning M, Brinks E, Kabisch J, Breitenwieser F, Lappann M, Bohnlein C, Franz CMAP	Microorganisms	10.3390/microorganisms9020246	2021
Phylogeny	Bacillus vini sp. nov. isolated from alcohol fermentation pit mud.	Ma K, Chen X, Guo X, Wang Y, Wang H, Zhou S, Song J, Kong D, Zhu J, Dong W, He M, Hu G, Zhao B, Ruan Z	Arch Microbiol	10.1007/s00203-016-1218-4	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 )
#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) .
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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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Heyndrickxia vini (ACCC 06413, JCM 19841)

Name and taxonomic classification

Morphology

Culture and growth conditions

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Isolation, sampling and environmental information

Isolation

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_016772275

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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