Lichenihabitans psoromatis (KCCM 43292, JCM 33310)

Name: Lichenihabitans psoromatis (KCCM 43292, JCM 33310)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 164515

Type strain

Culture col. no. KCCM 43292 JCM 33310

NCBI tax ID(s) 2528642

Special Collections JCM

Lichenihabitans psoromatis JCM 33310 is a psychrophilic prokaryote that was isolated from Lichen, Psoroma antarcticum.

psychrophilic genome sequence 16S sequence

Name and taxonomic classification

Culture Collection Numbers

KCCM 43292 JCM 33310

@ref 20215

Last LPSN update 2025-12-16 10:09:29

Domain Pseudomonadati

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Lichenihabitantaceae

Genus Lichenihabitans

Species Lichenihabitans psoromatis

Full scientific name Lichenihabitans psoromatis Noh et al. 2019

BacDive ID	Other strains from **Lichenihabitans psoromatis** (1)	Type strain
164516	L. psoromatis JCM 33311, KCCM 43293, PAMC 29148 (type strain)

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	98.6
125438	negative	98.315

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
67770	positive	growth	15	psychrophilic

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	98.5

Spore formation

@ref	Spore formation	Confidence
125439		96.6

Observation

67770

Observationquinones: Q-10

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Host species	Geographic location	Country	Country ISO 3 Code	Continent
67770	Lichen, Psoroma antarcticum	Psoroma antarcticum	King George Island	Antarctica	ATA	Antarctica

Taxonmaps

69479

Global distribution of 16S sequence MK344721 (>99% sequence identity) for Lichenihabitans psoromatis subclade from Microbeatlas

Aquatic Samples	194
Soil Samples	709
Animal Samples	178
Plant Samples	370
Total Samples	1451

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM432715v1 assembly for Lichenihabitans psoromatis PAMC 29128	scaffold	GCA_004327155	2528641.3	2889449010	2528642	67.24

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Lichenihabitans psoromatis strain PAMC29128 16S ribosomal RNA gene, partial sequence	MK344721	1414		2528642

GC content

@ref	GC-content (mol%)	Method
67770	63	genome sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Lichenihabitans psoromatis PAMC 29128
NCBI: GCA_004327155

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	96.60	no
125439	motility	BacteriaNetⓘ	no	55.70	no
125439	gram_stain	BacteriaNetⓘ	negative	98.60	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	98.50	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Lichenihabitans psoromatis PAMC 29128
NCBI: GCA_004327155.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	98.32	no
125438	anaerobic	anaerobicⓘ	no	94.43	no
125438	aerobic	aerobicⓘ	yes	82.46	no
125438	spore-forming	spore-formingⓘ	no	87.78	no
125438	thermophilic	thermophileⓘ	no	96.32	no
125438	flagellated	motile2+ⓘ	yes	58.78	no

Literature

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 )
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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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