Rickettsia conorii subsp. heilongjiangensis (HLJ-054, CSUR 054, 054)

Name: Rickettsia conorii subsp. heilongjiangensis (HLJ-054, CSUR 054, 054)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 166570

Type strain

Strain Designation HLJ-054, 054

Culture col. no. HLJ-054 CSUR 054 054 ATCC VR-1524

NCBI tax ID(s) 226665

Rickettsia conorii subsp. heilongjiangensis HLJ-054 is a bacterium of the family Rickettsiaceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

Culture Collection Numbers

HLJ-054 CSUR 054 054 ATCC VR-1524

@ref 20215

Last LPSN update 2026-05-29 08:45:56

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rickettsiales

Family Rickettsiaceae

Genus Rickettsia

Species Rickettsia conorii subsp. heilongjiangensis

Full scientific name Rickettsia conorii subsp. heilongjiangensis (Fournier et al. 2006) Hördt et al. 2020

Synonyms (2)

Rickettsia heilongjiangensis
"Rickettsia heilongjiangii"

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	92.117
125438	negative	96.458

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	98.524

Spore formation

@ref	Spore formation	Confidence
125438		96.297
125439		98.048

Isolation, sampling and environmental information

Taxonmaps

69479

Global distribution of 16S sequence AF178037 (>99% sequence identity) for Rickettsia from Microbeatlas

Aquatic Samples	1126
Soil Samples	494
Animal Samples	2180
Plant Samples	561
Total Samples	4361

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
20215	3	Risk group

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM22120v1 assembly for Rickettsia conorii subsp. heilongjiangensis 054	complete	GCA_000221205	1032845.11	650716082	1032845	99.57

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20215	Rickettsia heilongjiangensis strain HLJ-054 16S ribosomal RNA gene, partial sequence	AF178037	1469		226665

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM22120v1 assembly for Rickettsia conorii subsp. heilongjiangensis 054
NCBI: GCA_000221205

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	motility	BacteriaNetⓘ	no	70.79	no
125439	gram_stain	BacteriaNetⓘ	negative	92.12	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.52	no
125439	spore_formation	BacteriaNetⓘ	no	98.05	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Rickettsia conorii subsp. heilongjiangensis 054 054
NCBI: GCA_000221205.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.46	no
125438	anaerobic	anaerobicⓘ	no	82.33	no
125438	aerobic	aerobicⓘ	no	63.80	no
125438	spore-forming	spore-formingⓘ	no	96.30	no
125438	thermophilic	thermophileⓘ	no	93.75	no
125438	flagellated	motile2+ⓘ	no	82.20	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Genetics	Analysis of 1,000+ Type-Strain Genomes Substantially Improves Taxonomic Classification of Alphaproteobacteria.	Hordt A, Lopez MG, Meier-Kolthoff JP, Schleuning M, Weinhold LM, Tindall BJ, Gronow S, Kyrpides NC, Woyke T, Goker M.	Front Microbiol	10.3389/fmicb.2020.00468	2020
	Gene sequence-based criteria for identification of new rickettsia isolates and description of Rickettsia heilongjiangensis sp. nov.	Fournier PE, Dumler JS, Greub G, Zhang J, Wu Y, Raoult D.	J Clin Microbiol	10.1128/jcm.41.12.5456-5465.2003	2003

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) .
#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 .

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Rickettsia conorii subsp. heilongjiangensis (HLJ-054, CSUR 054, 054)

Name and taxonomic classification

Culture Collection Numbers

Morphology

Cell morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000221205

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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